Pharmaceutical Software Development Services

Computools delivers pharmaceutical software development services for manufacturers, distributors, and pharma networks, building custom solutions from drug lifecycle management systems to regulatory compliance platforms. Our software helps pharma businesses run smarter, stay compliant, and bring treatments to market faster

PHARMACEUTICAL SOFTWARE ENGINEERED FOR COMPLIANCE AND PERFORMANCE

45%

STREAMLINED PHARMACEUTICAL DISPENSING AND STOCK MANAGEMENT

Achieved through automated prescription workflows, real-time inventory tracking, and barcode-integrated dispensing systems that reduce delays and errors.

60%

LOWER OPERATIONAL COSTS

Enabled by centralized pharmaceutical dashboards, billing integration, and intelligent inventory management tools that minimize waste and overstock.

250+

EXPERTS ON BOARD

100% of our clients scale their pharmaceutical operations with us, trusting our domain expertise, regulatory knowledge, and focus on medication safety.

20+

PROJECTS DELIVERED

Custom pharmaceutical solutions built for manufacturers, distributors, and healthcare networks across global markets.

We are just as good as our clients say we are because their success is the true measure of ours.

SOLVING PHARMACEUTICAL BIGGEST CHALLENGES WITH CUSTOM SOFTWARE SOLUTIONS

Pharmaceutical companies face numerous challenges in a rapidly evolving landscape, with increasing demands for research and development of new drugs. Rising R&D costs, stricter regulatory requirements, changing patient expectations, and global supply chain instability are creating significant pressure on the industry. However, digital innovation and data-driven strategies offer new opportunities. They enable faster drug development, optimize operational processes, enhance clinical trial efficiency, and improve collaboration between teams. Through these technologies, companies can accelerate the creation of new drugs, reduce costs, and ensure high levels of safety and quality. Advanced technology solutions, including AI, play a crucial role in development and validation, creating a significant impact at every stage of the product lifecycle.
R&D
01 / 11
01. Automation of Laboratory Data Processing & Search

Challenges:

  • Slow Analysis & High Error Rate: Manual data handling slows down research workflows and introduces frequent mistakes, especially when dealing with large experiment volumes or inconsistent input formats.
  • Time-Consuming Search for Relevant Insights: Locating specific records or drawing insights from unstructured data takes significant time — especially when filtering options are limited or data is not uniformly tagged or categorized.
  • High Risk of Data Errors: Without standardized processes, manual entry and inconsistent handling lead to input mistakes, misinterpretations, or even data loss — ultimately compromising research quality and reproducibility.
  • Lack of Integration with Databases: When lab results remain siloed and disconnected from internal or external databases, researchers face duplicated work, limited context, and poor visibility into historical or comparative records.

Solution:

Custom AI Data Processing & Search System

Accelerate your lab workflows with a tailored automation solution that scans, structures, and filters research data using metadata-driven algorithms. Unlike generic off-the-shelf tools, we build systems designed around your specific data types, collection methods, and lab formats — ensuring seamless search, sorting, and cross-referencing.

From experiment logs and sensor outputs to biomedical records, our platform automatically processes incoming data and links it with internal or open-access knowledge bases, giving your team instant, contextual access to the full research picture.

Benefits:

  • Metadata-Based Search – Instantly find relevant records by tags like date, experiment type, compound, or researcher
  • Automatic Data Indexing – New data is processed and structured upon entry, with no manual effort
  • Database Integration – Syncs with internal and public repositories for contextual enrichment and historical comparison
  • Error Prevention Mechanisms – Smart algorithms detect anomalies and reduce human-factor errors
  • Custom Filters & Analytics – Built-in dashboards for filtering, visualizing, and exporting data according to your team’s needs

ROI impact:

  • Up to 60% reduction in time spent on manual data handling
  • 2x faster pattern and anomaly detection in large datasets
  • Improved reproducibility through structured and standardized data
  • Fewer data errors — leading to more confident, verifiable results
02. Intelligent Search for Previous Research and Cases

Challenges:

  • Repetition of Experiments Due to Lack of Knowledge: In large companies, new teams often conduct the same research that has already been done — simply because they cannot find or are unaware of previous work. This leads to wasted time, resources, and missed valuable insights.
  • Files Described Differently — Search Doesn’t Work: Even if the necessary information exists, it is hard to find because records and reports were created by different teams in various ways — using different terminology, titles, or formats.
  • Inaccessibility of Data from Internal Knowledge Bases: Past research results are often stored in outdated repositories, isolated databases, or without a user-friendly interface to search by content.
  • Limited Keyword Search: Traditional internal search engines only search for exact words in titles or text, ignoring context or semantic matches.

Solution:

Custom AI Search Engine with Natural Language Understanding (NLU)

We build an intelligent search engine that enables teams to quickly find relevant research, files, experiments, or graphs — even if they are named differently or described in another way.Such solutions integrates with internal repositories, databases, and file systems of the company and conducts searches within scientific articles and publicly available research. It analyzes content, context, and associated metadata to provide a comprehensive picture of previous studies. The system also learns over time, improving search accuracy and understanding of corporate terminology.

Benefits:

  • Semantic Search: Results are based on content, not just keywords
  • Natural Language Processing: You can ask questions in natural language without needing technical phrasing
  • Deep Integration: Search encompasses reports, graphs, presentations, spreadsheets, and internal databases
  • Automatic Categorization: Identifies recurring themes, hypotheses, and conclusions across similar projects
  • Improved Planning of All Complex Phases for Preclinical Research

ROI impact:

  • 30–50% reduction in repeated or duplicate experiments
  • 40–60% faster access to relevant information when planning new research
  • Significant reduction in time wasted by new teams
  • Improved decision-making quality based on past experiences
03. AI In Silico Screening

Challenges:

  • Large number of molecules to be tested: The volume of molecules that need to be tested is significantly increasing, requiring a lot of time and resources for screening. This complicates the selection of promising candidates and increases the likelihood of missing important molecules.
  • Delays in the selection process: Without the right tools for optimization, the molecule selection process takes more time, delaying subsequent stages of research and reducing overall research efficiency.
  • Risk of inefficient testing: A large number of tests on unpromising molecules can lead to wasted time and resources.

Solution:

Custom Models for Molecule Analysis:

Developing custom AI models to analyze large datasets of chemical and biological data. These models allow researchers to predict properties of molecules such as toxicity, bioavailability, and effectiveness based on historical data and previous experiments. These solutions help significantly reduce the number of tests by focusing attention on the most promising candidates.

Benefits:

  • Accelerated R&D Pipeline: Reduce molecule selection time by 30–50%.
  • Improved Hit-to-Lead Ratio: Focus on the most promising candidates from the start.
  • Smarter, Data-Driven Decisions: Minimize trial and error with predictive, AI-powered insights.
  • Cost Efficiency: Cut down on unnecessary testing and resource waste.

ROI impact:

  • Reduces molecule selection time by 30-50%.
  • Decreases testing costs through process optimization.
  • Increases chances of successful outcomes by focusing on the most promising candidates.
04. In Vitro Testing Challenges

Challenges:

  • Time-Consuming and Unpredictable Tests: Cell-based assays are slow and resource-intensive. Unpredictable outcomes complicate decision-making and lead to costly delays.
  • Limited Reproducibility: Variability in results makes it hard to draw consistent conclusions, slowing research and increasing uncertainty.
  • High Resource Burn: Repeating tests to confirm results drains time, money, and lab capacity — limiting scalability.

Solution:

Custom  AI-Driven In Vitro Research

Strengthen your in vitro testing processes with custom AI models that analyze and predict outcomes for a range of tests, including drug interactions with cell cultures, toxicity analysis of chemical compounds, enzymatic activity studies, and genetic or biochemical reactions in controlled environments. By leveraging both historical and real-time data, these models reduce the need for repetitive testing, streamline decision-making, and accelerate the path to reliable results.

Benefits:

  • Accurate Predictions – AI models provide highly accurate forecasts on molecule reactions with cell lines, reducing experimental uncertainty.
  • Custom Models – Tailored models are developed based on specific research needs, ensuring relevance and precision in results.
  • Reduced Testing Volume – With AI-based predictions, fewer experimental tests are necessary, significantly reducing resource consumption and time.
  • Improved Efficiency – AI accelerates research timelines by predicting outcomes faster and streamlining the process of selecting the most promising molecules.

ROI impact:

  • 20–30% reduction in testing time – AI reduces the need for repetitive experiments, saving time for other critical research activities.
  • 35–50% cost savings – Fewer tests lead to lower resource consumption and financial expenses.
  • Enhanced predictive accuracy – AI-driven models increase the likelihood of obtaining successful test results, boosting research confidence.
  • Faster time-to-market – AI supports quicker decision-making and speeds up the research-to-product timeline.
05. In Vivo Planning and Analytics

Challenges:

  • Difficulty in Selecting the Right Research Subjects: Identifying the most relevant organisms and conditions for testing new molecules can be challenging. This requires careful selection to avoid wasted resources and ensure accurate predictions.
  • Complexity of Predicting Organism Reactions: Predicting how an organism will react to new molecules without animal testing is a complex process that traditionally requires expensive and time-consuming physical experiments.
  • High Costs and Resource Demands: Animal testing is costly and consumes significant time and resources, slowing research progress.

Solution:

AI for In Vivo Planning and Analysis

AI enables intelligent in vivo planning by analyzing historical and real-time data from previous experiments, optimizing testing conditions, and conducting data searches to identify the most relevant organisms and conditions for new molecule testing. This solution allows researchers to predict the organism’s reaction to new molecules without the need for physical tests. AI helps select the most promising candidates for experimentation, minimizing required tests and enhancing research efficiency.

Benefits:

  • Optimized Testing Conditions: AI assists in selecting the best conditions for each study, increasing the accuracy of results.
  • Faster, More Efficient Research: By reducing the number of required tests, AI speeds up research and lowers costs.
  • Enhanced Data Search Capabilities: AI facilitates advanced data searching to find the most relevant studies, ensuring more targeted research.

Title ROI impact:

  • 30-50% reduction in animal testing costs: Minimizing animal testing leads to substantial savings in resources and lab costs.
  • 30-40% reduction in the number of required experiments: Fewer tests needed, optimizing resource allocation and reducing time.
  • 20-30% faster time-to-results: AI-based insights accelerate research timelines, leading to quicker decision-making.
06. Modeling Potential Drug Interactions

Challenges:

  • Unpredictable side effects from drug combinations: Some drugs may interact with each other in a way that causes harmful side effects, even if they work well individually.
  • Manual drug interaction assessment: Currently, healthcare providers rely on outdated or incomplete resources to manually check potential drug interactions, which can lead to missed risks.
  • Complexity of drug properties: Assessing drug interactions requires considering many factors, including chemical properties, biological mechanisms, and individual patient characteristics, which makes manual evaluation challenging.
  • Insufficient data integration: Many existing systems do not effectively integrate chemical, pharmacological, and patient data, limiting the ability to predict or prevent dangerous drug interactions.

Solution:

Drug Interaction Custom Prediction Models

Utilizing AI to create predictive models that analyze chemical properties, biological effects, and historical data to predict potential interactions between different drugs. We develop custom solutions that use machine learning algorithms trained on large datasets to forecast how drugs might interact in the human body, helping healthcare providers avoid dangerous combinations.
We create personalized drug interaction prediction systems that integrate chemical, pharmacological, and patient data. By analyzing known interactions and the individual conditions of the patient, we provide healthcare providers with valuable insights that help prevent harmful effects from drug combinations.

Benefits:

  • AI-based drug interaction predictions: Predicting harmful interactions based on chemical properties, biological effects, and individual patient characteristics.
  • Real-time alerts for healthcare providers: Automatic detection of dangerous combinations during prescription creation or modification.
  • Data integration from multiple sources: Access to integrated data from chemistry, biology, and patient history for more accurate predictions.
  • Personalized recommendations: Tailored drug selection based on individual patient characteristics, optimizing treatment safety.

ROI impact:

  • Reduction of adverse drug events by 25–40%: Early prediction of interactions helps prevent harmful side effects and hospitalizations.
  • Improvement in prescribing accuracy by 20–30%: Healthcare providers receive reliable insights, reducing human error when selecting drugs.
  • Increased safety and patient satisfaction: Reducing the risks of harmful drug interactions enhances patient trust in healthcare services.
07. Data-Driven Bioprocess Design

Challenges:

  • Conducting physical experiments with new molecules or components is expensive and time-consuming.
  • Limited number of experimental variations makes it difficult to select the optimal testing scenario.
  • Decisions are often based on a small number of trials, which do not account for all variables.

Solution:

Custom AI-Powered Data Insight Platform

A custom AI-powered data analysis platform that uses artificial intelligence to identify correlations, patterns, and hidden dependencies in your historical experimental data. This enables better understanding of processes, optimization of bioprocess experimental options, and more informed decision-making without the need for costly physical testing.

Benefits:

  • Saves time and resources by reducing the number of physical experiments required.
  • Enhances decision-making through data analysis and AI algorithms.
  • Optimizes focus on the most promising experiments.
  • Scalable to handle large and complex datasets.

ROI impact:

  • Reduces experimental costs by 20-30%.
  • Cuts time spent on physical testing by 15-25%.
  • Improves accuracy of predictions and quality of results.
08. AI-Driven Clinical Trial Data Analysis

Challenges:

  • Unstructured and Complex Data: Clinical trial data is large and complex. Without proper analysis, it’s difficult to identify useful patterns for drawing conclusions.
  • Overwhelming Volume of Data: The large volume of data makes manual processing difficult, increasing analysis time and reducing the ability to identify insights effectively.
  • Data Integration and Pattern Recognition: Integrating data from various sources to build a unified view of drug efficacy is challenging.

Solution:

Implement an AI-powered solution that integrates clinical trial data from diverse sources, such as patient demographics, biomarkers, medical histories, treatment protocols, and clinical outcomes. The system uses machine learning algorithms to analyze historical and ongoing data, automatically recognizing patterns within different patient groups and predicting treatment outcomes. It combines data from multiple clinics and sources into a unified platform, enabling a comprehensive view of the trial, improving data integration and correlation. This allows for faster, more accurate analysis, enhances decision-making by providing visual insights, and ultimately leads to more informed choices regarding drug efficacy and patient safety.

Benefits:

  • Faster Data Processing: Automated data processing speeds up decision-making.
  • Improved Pattern Detection: Identifying correlations between treatment efficacy and patient demographics.
  • Enhanced Decision-Making: Visualizations and analytics make it easier to make informed decisions regarding drug safety and efficacy.
  • Integrated Data Sources: Combining data from multiple clinics provides a comprehensive view of the trial.

ROI impact:

  • 25-30% Faster Analysis: Faster data analysis reduces time to decision-making.
  • 15-20% Reduction in Costs: Lower costs in data processing thanks to automation.
  • Increased Success Rates: Identifying effective patterns earlier improves drug development success.
  • Reduced Risk of Adverse Events: Early identification of risks decreases the likelihood of side effects.
09. Regulatory Documentation Management

Challenges:

  • Version Control Issues: In large research teams, tracking the latest version of documents is difficult, leading to the use of outdated files and compromising data integrity.
  • Fragmented Document Storage: Documentation is often spread across disconnected systems, making search and access time-consuming—especially critical during audits or regulatory reviews.
  • Data Privacy Risks: Poor security practices can result in leaks of sensitive patient or study data, leading to legal and reputational consequences.
  • Lack of Visibility into Regulatory Changes: Tracking updates from global regulators is a manual, error-prone process. Failure to react to changes quickly can result in non-compliance and costly remediation.

Solution:

Custom AI-Driven Regulatory Documentation Management 

We propose a custom AI-assisted Regulatory Intelligence and Document Management System designed to support your team in managing compliance throughout the entire research lifecycle. The system helps monitor global regulatory sources and quickly identify data relevant to new or updated requirements. Once updates are detected, it assists in locating associated internal documents and processes that may need to be revised. The system streamlines document navigation, highlights what requires review or re-approval, and recommends next steps based on predefined workflows.

Benefits:

  • Smart Document Linking: Automatically connects regulatory changes to related SOPs, documents, or trial stages requiring attention.
  • Centralized Document Access: A unified, searchable, version-controlled repository ensures easy access to all relevant documentation.
  • Data Privacy & Security: Compliant with standards like 21 CFR Part 11 and GDPR, providing secure handling of sensitive data.
  • Integrated Collaboration Tools: Role-based access, annotations, and change tracking simplify both internal reviews and external regulatory audits.
  • Reduced  manual workload, improves traceability, and keeps your team aligned with evolving regulatory expectations.

ROI impact:

  • 30–40% Reduction in Compliance Risks: Proactive tracking minimizes the likelihood of regulatory violations.
  • 20–25% Time Savings: Automation of mapping and version control streamlines compliance workflows.
  • 15–20% Cost Reduction: Fewer delays, fewer document errors, and less dependency on external consultants.
  • Improved Audit Readiness: All activities are logged, traceable, and audit-ready at any time.
10. Lack of Proper Data Synchronization Between Departments

Challenges:

  • Fragmented data storage systems: Departments involved in clinical trials, licensing, analytics, or formulation development often operate in siloed data environments. This leads to data duplication, loss of information, and difficulty in making informed decisions based on the full picture.
  • Delays in decision-making: Without centralized data access, collaboration between teams slows down, causing project delays and reducing overall research efficiency.
  • Reporting and analytics: Without a single source of truth, analytics become incomplete or inconsistent, limiting transparency of research results and complicating project performance tracking.

Solution:

Custom Integrated Data Management Platform

Break down departmental barriers by implementing a unified, automated data management platform that ensures seamless synchronization across all R&D departments and is fully tailored to your organizational structure and needs.

We design custom solutions that consolidate data from research, experimental, laboratory, preclinical, clinical, and analytical areas into one environment. This enables faster collaboration, more accurate analytics, and full regulatory compliance.

Benefits:

  • Centralized data access – A single control panel with role-based access
  • Real-time synchronization via API – Instant updates across all systems
  • Complete change traceability – Automatic logging of modifications and approvals to ensure compliance
  • Team collaboration tools – Shared reviewing, commenting, and data exchange across departments
  • Flexible integration – Adapts to both legacy and modern systems in your infrastructure

ROI impact:

  • 25–40% faster decision-making across teams
  • 30–50% reduction in data loss and duplication
  • Increased audit readiness and documentation quality
  • Enhanced cross-team collaboration throughout the research lifecycle
11. IoT Solution for Real-Time Data Analysis

Challenges:

  • High Volume of Data Processing: Manual processing of large volumes of experimental data leads to delays and inconsistencies, making it challenging to manage and analyze real-time data effectively.
  • Limited Process Control: Traditional systems require manual efforts to maintain optimal conditions, leading to inefficiencies and a lack of adaptive capacity during experiments.
  • Slow Response to Changes: With manual tracking and data processing, it’s difficult to quickly adjust process variables or respond to unexpected results during experiments.

Solution:

Custom IoT Systems

Utilize an IoT-based solution to collect and analyze real-time data from lab equipment and biosensors. By integrating sensors that monitor critical variables such as pH, temperature, oxygen levels, and nutrient concentrations, this system allows researchers to track and adjust experimental conditions continuously. It provides actionable insights for optimized decision-making, automating responses to changing conditions and ensuring more efficient experiment management.

Benefits:

  • Accelerated Decision-Making: Real-time insights from IoT-connected devices enable quicker response to any variances in experimental conditions, improving the pace of decision-making.
  • Reduced Human Error: Automated data collection and analysis reduce the possibility of human error, enhancing the reliability and reproducibility of experiments.
  • Real-Time Monitoring & Adjustment: Instant access to real-time data enables immediate adjustments to process variables, reducing the need for manual oversight.

ROI impact:

  • 30% Reduction in Testing Costs: Real-time data and automated adjustments significantly reduce the need for physical testing and the associated costs.
  • 20% Faster Time-to-Market: Improved process efficiency accelerates the time required for drug development and regulatory approvals.
  • 10–15% Improvement in Experiment Success Rate: Better control over experimental conditions increases the success rate and consistency of results.
  • Resource Optimization: IoT-driven insights ensure more efficient use of resources, reducing waste and improving overall productivity.
Manufacturing
01 / 07
01. Equipment Downtime & Maintenance

Challenges:

  • Frequent Unplanned Downtime: Critical pharmaceutical equipment—such as peristaltic pumps, autoclaves, filtration systems, and sterilization units—often fail unexpectedly, halting GMP-compliant processes and causing batch losses or delays.
  • Reactive Maintenance Risks: Emergency fixes are not only costly but may also require re-validation of processes or environmental conditions, affecting FDA/EMA compliance.
  • Manual Inspections Are Ineffective: Periodic checks often miss subtle anomalies. Failures detected late can compromise aseptic conditions and product sterility.
  • Regulatory Pressure: Any equipment failure that affects validated processes requires deviation management and thorough documentation.

Solution:

AI Predictive Maintenance

We develop a custom-tailored predictive maintenance solution specifically for pharmaceutical manufacturing, where IIoT sensors are installed on critical equipment like compressors, cleanroom systems, and sterilization units to monitor parameters such as temperature, vibration, and pressure in real time. This data feeds into an analytics engine trained on typical equipment behavior in regulated production environments, enabling early detection of potential issues and forecasting failures 7–14 days in advance. Maintenance can then be scheduled proactively during planned downtime. The solution integrates with existing production and quality systems, automatically triggering service tasks and ensuring traceable, audit-ready maintenance workflows.

Benefits:

  • Up to 40% reduction in unplanned downtime, minimizing batch rework and release delays.
  • 15% savings in annual maintenance costs, through proactive servicing and fewer emergency repairs.
  • Improved regulatory compliance, with real-time documentation, deviation prevention, and full traceability.
  • Reduced need for revalidation, as equipment is maintained before failure, preserving process integrity.
  • Better OEE and resource utilization, ensuring manufacturing continuity and audit-readiness.

ROI impact:

  • 40% fewer production interruptions due to equipment failure
  • Up to 20% decrease in batch rejection rates due to environmental deviations
  • 30% improvement in maintenance scheduling efficiency
  • Stronger compliance posture for FDA, EMA, and MHRA audits
02. Product Defects & Inspection Challenges

Challenges:

  • Visual Defects and Contamination: Tablets, capsules, and vials can exhibit chips, cracks, discoloration, or foreign-particle contamination that traditional manual inspection often misses or flags too late in the line.
  • Label and Print Verification: Misprints in batch numbers, expiration dates, or barcodes lead to recalled batches and compliance violations; manual OCR/OCV checks are slow and error-prone under high-speed conditions.
  • Fill Level and Seal Integrity: Under- or over-filled vials and improperly crimped seals risk dosage errors and microbial ingress. Manual gauging or random sampling fails to guarantee 100 % inspection coverage.
  • Throughput vs. Accuracy: High-speed production lines make it impossible for human inspectors to maintain > 99 % defect-detection accuracy without causing bottlenecks

Solution:

Custom Vision AI System

We develop a custom Vision AI system that seamlessly integrates with existing pharmaceutical production lines to enable real-time quality control. The system automatically detects shape defects, color inconsistencies, and foreign particles in tablets, capsules, and vials; verifies label accuracy, serial numbers, barcodes, and expiration dates without stopping the line; inspects liquid fill levels and cap integrity by comparing each unit to a reference image; and removes defective units from the line using robotic arms. All data is captured and securely stored to ensure full traceability and compliance.

Benefits:

  • Near-Perfect Defect Detection: Over 99 % accuracy in identifying visual defects and contamination, reducing escapes to downstream operations.
  • Full-Line Coverage: Automating inspection of every unit eliminates sampling gaps, ensuring consistent quality even at high throughput.
  • Regulatory Compliance: Automated audit trails and digital records of every inspection meet 21 CFR Part 11 traceability requirements.
  • Reduced Waste & Rework: Early removal of defective items lowers scrap rates  and minimizes costly batch recalls.
  • Operational Efficiency: AI-driven inspection reduces manual labor, reallocating staff to value-added tasks

ROI impact:

  • 15–25 % Reduction in Scrap Costs through accurate early defect removal.
  • 20–30 % Increase in Throughput by eliminating manual bottlenecks and inspections.
  • 10–15 % Decrease in Compliance Penalties by ensuring 100 % label and seal verification.
  • Rapid Payback: Most implementations achieve ROI within 6–12 months due to savings in labor, waste, and rework.
03. Inventory Management & Tracking

Challenges:

  • Manual Counting Limitations: Traditional mechanical or manual counting systems for vials, ampoules, capsules, or boxes are slow, error-prone, and often require line stoppages or human supervision. In high-speed pharmaceutical environments, even minor miscounts can impact batch integrity and inventory planning.
  • Lack of Real-Time Visibility: Inventory data is often updated post-factum, leading to delays in stock replenishment. Without real-time feedback from the production line, companies risk shortages of critical materials, causing production halts.
  • Disconnected Systems: Counting devices, production systems, and warehouse management platforms (WMS) are often isolated, making automated inventory reconciliation difficult. Manual data entry introduces errors and delays in synchronization.

Solution:

AI-Powered Inline Counting System

We implement vision-enabled inline counting systems that use advanced Vision AI to count pharmaceutical units — vials, ampoules, capsules, or packaging — in real time with <1% error margin. This eliminates the need for bulky traditional counters and reduces human involvement on the line. The counting data is instantly transmitted to the warehouse management system (WMS), allowing automated stock updates, triggering reorder workflows, and ensuring production continuity by preventing stockouts of critical components.

Benefits:

  • Automated Inventory Updates: Count data flows directly into the Warehouse Management System (WMS), enabling automatic inventory reconciliation and real-time stock visibility.
  • Faster Replenishment Cycles: Low-stock thresholds trigger automatic restock requests, minimizing downtime and preventing production delays due to missing materials.
  • Improved Operational Efficiency: With seamless integration between production and warehouse systems, the plant runs leaner and reacts faster to demand fluctuations.

ROI impact:

  • 80–90% Reduction in Manual Counting Time
  • >95% Inventory Accuracy Across Batches
  • 15–20% Reduction in Emergency Procurement Costs
  • Fewer Line Stops Due to Stockouts
04. Regulatory Complexity

Changes:

  • Large pharmaceutical sites face constant updates to FDA, EMA, and other global regulations, often requiring updates to documentation and requalification of processes, which can delay production.
  • Version Control Issues: Managing the latest versions of SOPs, batch records, and validation protocols across multiple shifts and facilities is error-prone. Using outdated documents can result in non-compliance during audits.
  • Fragmented Document Storage: Critical documentation, such as e-Batch Records and electronic signatures, is stored across different systems (QMS, LIMS, MES), making retrieval and search difficult, especially during audits.
  • Audit Readiness and Traceability: Manual or semi-automated processes for capturing audit trails and signatures create gaps in traceability. Demonstrating compliance with 21 CFR Part 11 often requires significant retrospective data collection.

Solution:

Custom Digital Compliance Platform

We create platforms that integrate with existing QMS, MES, LIMS, and ERP systems to automate regulatory change monitoring, document version control, and real-time audit-ready reporting. The platform continuously tracks global regulatory sources, flags relevant changes, and links them to affected SOPs, batch templates, and validation protocols, guiding experts through necessary updates. The e-Batch Records module automatically collects data from production lines, eliminating manual entry errors, and captures immutable audit trails and compliant electronic signatures.

Benefits:

  • Automated Regulatory Intelligence: AI monitors global regulatory updates, reducing manual tracking by up to 80%.
  • Centralized Version-Controlled Repository: A single searchable document store with version control and role-based access.
  • Seamless e-Batch Records & Audit Trails: Real-time capture of manufacturing data and signatures ensures traceability and batch release readiness.
  • Compliance Dashboard & Alerts: Live dashboards highlight regulatory deadlines and non-conformities, with automated alerts for timely reviews and re-approvals.

ROI impact:

  • 30-40% Reduction in Compliance Risk
  • 20-25% Time Savings in Document Management
  • 15-20% Cost Reduction in Compliance Operations
  • Improved Audit Readiness
05. High Energy Consumption

Challenges:

  • High HVAC and Process Energy Loads: Pharmaceutical facilities allocate a significant share of energy to HVAC and steam systems, which often operate at fixed set-points regardless of real-time demand, leading to wasted energy.
  • Lack of Real-Time Visibility: Without continuous monitoring, inefficiencies like overuse of chillers or fans remain hidden, hindering targeted energy optimization.
  • Static Control Strategies: Traditional BMS systems rely on fixed schedules or manual tuning, missing dynamic adjustments based on production needs or ambient conditions.
  • Siloed Data and Limited Integration: Disconnected energy, MES, and BMS data prevent comprehensive analysis of how batch operations and shift activities affect energy consumption.

Solution:

Custom  Smart Energy Management Platform

We develop smart energy management platforms that integrate with existing BMS, MES, ERP, and IoT infrastructures to enable real-time monitoring, automated control, and predictive energy optimization. The platform continuously collects data from HVAC systems, chillers, and process equipment via wireless sensors, analyzing it with AI algorithms to detect inefficiencies, predict performance degradation, and recommend optimal operating parameters based on production schedules and environmental conditions. Interactive dashboards provide instant visibility into energy consumption per batch or shift, while automated alerts notify teams about abnormal patterns or required maintenance, helping pharmaceutical plants reduce energy costs and improve sustainability.

Benefits:

  • 10–15% Overall Energy Savings by minimizing idle time and optimizing schedules
  • 30% Reduction in Unplanned Downtime through predictive maintenance alerts
  • Shift- and Batch-Level Transparency for energy consumption, enabling accountability and continuous improvement
  • Automated Sustainability Reporting for GHG compliance with minimal manual effort

ROI impact:

  • Up to 14% Energy Cost Reduction in the first six months
  • 20–25% Lower Maintenance Costs by moving from reactive to predictive maintenance
  • 5–10% Production Throughput Gain from stabilized temperature control and reclaimed runtime
  • Accelerated Payback Period
06. Waste and Emissions Management

Challenges:

  • Rising Disposal Costs: Large-scale pharmaceutical manufacturing and pilot lines generate substantial volumes of hazardous and chemical waste. Disposal costs are increasing annually, putting pressure on operational budgets.
  • Manual Classification & Tracking: Waste stream logging and categorization are often manual, time-consuming, and error-prone, leading to misclassification and the risk of regulatory penalties.
  • Regulatory Reporting Burden: Environmental compliance requires detailed waste manifests and regulatory submissions. Fragmented systems delay reporting and increase the risk of audit issues.
  • Sustainability & Packaging Waste: Pharma packaging contributes significantly to plastic waste, raising both environmental and reputational concerns.

Solution:

Custom Digital Waste Management Platform

We build purpose-driven waste management modules that integrate with MES, LIMS, and ERP systems to automate the classification, tracking, and reporting of pharmaceutical waste. Real-time production and lab data feed into the platform to categorize waste accurately, auto-generate compliant documentation, and provide centralized dashboards for audit readiness. Integrated sustainability analytics track packaging use and support the shift toward recyclable and biodegradable alternatives.

Benefits:

  • Up to 18% Reduction in Disposal Costs: Optimized waste categorization avoids excessive hazardous waste fees.
  • 50% Faster Audit Preparation: Centralized, immutable waste records and automated reports streamline audit readiness.
  • 25% Less Plastic Waste Over 3 Years: Sustainability tools drive packaging material changes, cutting plastic usage significantly.
  • Improved Environmental Compliance: Real-time alerts for storage duration, inspections, and non-compliance events reduce risks.

ROI impact:

  • 18% Waste Disposal Cost Savings
  • 30% Reduction in Manual Processing Time
  • 20% Fewer Sustainability Audit Findings
07. Drug Delivery

Challenges:

  • Storage Conditions: Drugs, especially temperature-sensitive ones, require precise control of temperature and humidity during transportation and storage. Failure to maintain these conditions can lead to a loss of drug efficacy and regulatory violations.
  • Supply Chain Traceability: Lack of complete visibility at each stage of the supply chain can lead to errors or delays, especially in urgent situations such as disease outbreaks or changes in demand.
  • Regulatory Compliance: Compliance with regulatory bodies’ requirements for the storage and transportation of drugs requires accurate data and real-time access to documentation, which is challenging when using traditional systems.
  • Risk of Contamination and Counterfeiting: Drugs can be damaged or falsified during storage and transportation, jeopardizing patient safety.

Solution:

Custom IoT Platform for Drug Delivery Control

The development of an IoT platform that integrates with existing systems to track the storage conditions and transportation of drugs at every stage of the supply chain in real-time. Sensors are used to monitor temperature, humidity, and location, allowing for prompt responses to changes and ensuring compliance with storage conditions.

Benefits:

  • Real-Time Monitoring: IoT sensors provide continuous monitoring of temperature and humidity in transport containers and storage facilities, ensuring proper drug storage.
  • Complete Visibility: Full visibility of every stage of drug delivery with accurate time and location, enabling rapid response in case of issues.
  • Automated Notifications: The platform sends automatic notifications about any deviations from normal conditions (e.g., temperature exceeding acceptable limits), allowing for swift corrective actions.
  • Secure and Verified Delivery: The use of blockchain technology or cryptographic signatures ensures not only precise monitoring but also protection against drug falsification during transportation.

ROI impact:

  • Reduction in Drug Loss Due to Improper Storage Conditions by 30-40%.
  • Time Savings in Tracking and Documentation of Deliveries up to 30%.
  • Improved Regulatory Compliance
Support & Maintenance
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01. Pharmacovigilance and Drug Safety Monitoring

Challenges:

  • Detection and Management of Adverse Drug Effects: Difficulties in detecting and managing adverse drug reactions after drugs are released to the market.
  • Issues with Systematizing and Monitoring Drug Safety: Poor monitoring of the ongoing impact of drugs, particularly beyond the traditional clinical trial phases (Phase IV).
  • High Monitoring Costs: Increased costs for drug safety monitoring due to manual processes.
  • Risk of Unauthorized Use of Drugs: Potential for misuse of drugs and lack of oversight after their market release.

Solution:

Custom Pharmacovigilance System

An AI-powered platform for drug safety monitoring that uses data analytics collected from hospitals and consumer reports to identify adverse effects and enable quick responses.

Benefits:

  • Cost Reduction: Lower costs for safety monitoring by automating data analysis and reporting processes.
  • Improved Adverse Effect Detection: More accurate and faster detection of drug side effects.
  • Faster Response Times: Enhanced ability to quickly respond to emerging safety concerns.

ROI impact:

  • 15% Reduction in Safety Monitoring Costs.
  • 25% Decrease in Adverse Drug Effects.
  • 30% Improvement in Response Time.
02. Customer Support

Challenges:

  • Low Medication Consumption Promotion: Pharmaceutical companies often struggle with encouraging patients to consistently consume their medications. This issue may stem from patients forgetting doses, lack of understanding of medication importance, or insufficient engagement with the prescribed regimen.
  • Non-adherence to Treatment Regimens: Patients often fail to follow prescribed treatment plans, which impacts the effectiveness of their treatment.
  • Ineffective Treatment Outcomes: Treatment effectiveness decreases when patients do not receive consistent support or consultations.
  • High Costs of Physical Monitoring: Continuous physical monitoring of patient adherence can be expensive and resource-intensive.
  • Challenges in Managing Treatment Programs: Difficulty coordinating and managing individual treatment plans leads to inconsistencies in patient care.

Solution:

Custom Patient Support System 

A mobile app-based solution utilizing AI to support patients in adhering to their treatment plans. Such  platforms offer companies an effective tool for promoting both existing and new products to their patient base. It provides personalized recommendations and reminders tailored to specific conditions, helping patients track their medication intake, adjust dosages, and monitor their treatment progress. Patients can upload their test results and progress, allowing for more informed treatment adjustments.

Benefits:

  • Real-Time Data and Insights: The platform provides pharmaceutical companies with real-time data on medication adherence, patient progress, and treatment outcomes, offering valuable insights for product development, marketing strategies, and regulatory compliance
  • Enhanced Product Promotion: The mobile app allows seamlessly promote both new and existing products.
  • Increased Adherence: By offering patients personalized AI-driven reminders, dosage adjustments, and continuous progress tracking, pharmaceutical companies can ensure higher adherence rates to prescribed treatments, ultimately improving treatment outcomes and increasing patient retention.

ROI impact:

  • Increased Product Sales up to 30%
  • Reduction in Operational Costs by 20%
    Increased Patient Retention by 20-25%

Challenges:

  • Slow Analysis & High Error Rate: Manual data handling slows down research workflows and introduces frequent mistakes, especially when dealing with large experiment volumes or inconsistent input formats.
  • Time-Consuming Search for Relevant Insights: Locating specific records or drawing insights from unstructured data takes significant time — especially when filtering options are limited or data is not uniformly tagged or categorized.
  • High Risk of Data Errors: Without standardized processes, manual entry and inconsistent handling lead to input mistakes, misinterpretations, or even data loss — ultimately compromising research quality and reproducibility.
  • Lack of Integration with Databases: When lab results remain siloed and disconnected from internal or external databases, researchers face duplicated work, limited context, and poor visibility into historical or comparative records.

Solution:

Custom AI Data Processing & Search System

Accelerate your lab workflows with a tailored automation solution that scans, structures, and filters research data using metadata-driven algorithms. Unlike generic off-the-shelf tools, we build systems designed around your specific data types, collection methods, and lab formats — ensuring seamless search, sorting, and cross-referencing.

From experiment logs and sensor outputs to biomedical records, our platform automatically processes incoming data and links it with internal or open-access knowledge bases, giving your team instant, contextual access to the full research picture.

Benefits:

  • Metadata-Based Search – Instantly find relevant records by tags like date, experiment type, compound, or researcher
  • Automatic Data Indexing – New data is processed and structured upon entry, with no manual effort
  • Database Integration – Syncs with internal and public repositories for contextual enrichment and historical comparison
  • Error Prevention Mechanisms – Smart algorithms detect anomalies and reduce human-factor errors
  • Custom Filters & Analytics – Built-in dashboards for filtering, visualizing, and exporting data according to your team’s needs

ROI impact:

  • Up to 60% reduction in time spent on manual data handling
  • 2x faster pattern and anomaly detection in large datasets
  • Improved reproducibility through structured and standardized data
  • Fewer data errors — leading to more confident, verifiable results

Challenges:

  • Repetition of Experiments Due to Lack of Knowledge: In large companies, new teams often conduct the same research that has already been done — simply because they cannot find or are unaware of previous work. This leads to wasted time, resources, and missed valuable insights.
  • Files Described Differently — Search Doesn’t Work: Even if the necessary information exists, it is hard to find because records and reports were created by different teams in various ways — using different terminology, titles, or formats.
  • Inaccessibility of Data from Internal Knowledge Bases: Past research results are often stored in outdated repositories, isolated databases, or without a user-friendly interface to search by content.
  • Limited Keyword Search: Traditional internal search engines only search for exact words in titles or text, ignoring context or semantic matches.

Solution:

Custom AI Search Engine with Natural Language Understanding (NLU)

We build an intelligent search engine that enables teams to quickly find relevant research, files, experiments, or graphs — even if they are named differently or described in another way.Such solutions integrates with internal repositories, databases, and file systems of the company and conducts searches within scientific articles and publicly available research. It analyzes content, context, and associated metadata to provide a comprehensive picture of previous studies. The system also learns over time, improving search accuracy and understanding of corporate terminology.

Benefits:

  • Semantic Search: Results are based on content, not just keywords
  • Natural Language Processing: You can ask questions in natural language without needing technical phrasing
  • Deep Integration: Search encompasses reports, graphs, presentations, spreadsheets, and internal databases
  • Automatic Categorization: Identifies recurring themes, hypotheses, and conclusions across similar projects
  • Improved Planning of All Complex Phases for Preclinical Research

ROI impact:

  • 30–50% reduction in repeated or duplicate experiments
  • 40–60% faster access to relevant information when planning new research
  • Significant reduction in time wasted by new teams
  • Improved decision-making quality based on past experiences

Challenges:

  • Large number of molecules to be tested: The volume of molecules that need to be tested is significantly increasing, requiring a lot of time and resources for screening. This complicates the selection of promising candidates and increases the likelihood of missing important molecules.
  • Delays in the selection process: Without the right tools for optimization, the molecule selection process takes more time, delaying subsequent stages of research and reducing overall research efficiency.
  • Risk of inefficient testing: A large number of tests on unpromising molecules can lead to wasted time and resources.

Solution:

Custom Models for Molecule Analysis:

Developing custom AI models to analyze large datasets of chemical and biological data. These models allow researchers to predict properties of molecules such as toxicity, bioavailability, and effectiveness based on historical data and previous experiments. These solutions help significantly reduce the number of tests by focusing attention on the most promising candidates.

Benefits:

  • Accelerated R&D Pipeline: Reduce molecule selection time by 30–50%.
  • Improved Hit-to-Lead Ratio: Focus on the most promising candidates from the start.
  • Smarter, Data-Driven Decisions: Minimize trial and error with predictive, AI-powered insights.
  • Cost Efficiency: Cut down on unnecessary testing and resource waste.

ROI impact:

  • Reduces molecule selection time by 30-50%.
  • Decreases testing costs through process optimization.
  • Increases chances of successful outcomes by focusing on the most promising candidates.

Challenges:

  • Time-Consuming and Unpredictable Tests: Cell-based assays are slow and resource-intensive. Unpredictable outcomes complicate decision-making and lead to costly delays.
  • Limited Reproducibility: Variability in results makes it hard to draw consistent conclusions, slowing research and increasing uncertainty.
  • High Resource Burn: Repeating tests to confirm results drains time, money, and lab capacity — limiting scalability.

Solution:

Custom  AI-Driven In Vitro Research

Strengthen your in vitro testing processes with custom AI models that analyze and predict outcomes for a range of tests, including drug interactions with cell cultures, toxicity analysis of chemical compounds, enzymatic activity studies, and genetic or biochemical reactions in controlled environments. By leveraging both historical and real-time data, these models reduce the need for repetitive testing, streamline decision-making, and accelerate the path to reliable results.

Benefits:

  • Accurate Predictions – AI models provide highly accurate forecasts on molecule reactions with cell lines, reducing experimental uncertainty.
  • Custom Models – Tailored models are developed based on specific research needs, ensuring relevance and precision in results.
  • Reduced Testing Volume – With AI-based predictions, fewer experimental tests are necessary, significantly reducing resource consumption and time.
  • Improved Efficiency – AI accelerates research timelines by predicting outcomes faster and streamlining the process of selecting the most promising molecules.

ROI impact:

  • 20–30% reduction in testing time – AI reduces the need for repetitive experiments, saving time for other critical research activities.
  • 35–50% cost savings – Fewer tests lead to lower resource consumption and financial expenses.
  • Enhanced predictive accuracy – AI-driven models increase the likelihood of obtaining successful test results, boosting research confidence.
  • Faster time-to-market – AI supports quicker decision-making and speeds up the research-to-product timeline.

Challenges:

  • Difficulty in Selecting the Right Research Subjects: Identifying the most relevant organisms and conditions for testing new molecules can be challenging. This requires careful selection to avoid wasted resources and ensure accurate predictions.
  • Complexity of Predicting Organism Reactions: Predicting how an organism will react to new molecules without animal testing is a complex process that traditionally requires expensive and time-consuming physical experiments.
  • High Costs and Resource Demands: Animal testing is costly and consumes significant time and resources, slowing research progress.

Solution:

AI for In Vivo Planning and Analysis

AI enables intelligent in vivo planning by analyzing historical and real-time data from previous experiments, optimizing testing conditions, and conducting data searches to identify the most relevant organisms and conditions for new molecule testing. This solution allows researchers to predict the organism’s reaction to new molecules without the need for physical tests. AI helps select the most promising candidates for experimentation, minimizing required tests and enhancing research efficiency.

Benefits:

  • Optimized Testing Conditions: AI assists in selecting the best conditions for each study, increasing the accuracy of results.
  • Faster, More Efficient Research: By reducing the number of required tests, AI speeds up research and lowers costs.
  • Enhanced Data Search Capabilities: AI facilitates advanced data searching to find the most relevant studies, ensuring more targeted research.

Title ROI impact:

  • 30-50% reduction in animal testing costs: Minimizing animal testing leads to substantial savings in resources and lab costs.
  • 30-40% reduction in the number of required experiments: Fewer tests needed, optimizing resource allocation and reducing time.
  • 20-30% faster time-to-results: AI-based insights accelerate research timelines, leading to quicker decision-making.

Challenges:

  • Unpredictable side effects from drug combinations: Some drugs may interact with each other in a way that causes harmful side effects, even if they work well individually.
  • Manual drug interaction assessment: Currently, healthcare providers rely on outdated or incomplete resources to manually check potential drug interactions, which can lead to missed risks.
  • Complexity of drug properties: Assessing drug interactions requires considering many factors, including chemical properties, biological mechanisms, and individual patient characteristics, which makes manual evaluation challenging.
  • Insufficient data integration: Many existing systems do not effectively integrate chemical, pharmacological, and patient data, limiting the ability to predict or prevent dangerous drug interactions.

Solution:

Drug Interaction Custom Prediction Models

Utilizing AI to create predictive models that analyze chemical properties, biological effects, and historical data to predict potential interactions between different drugs. We develop custom solutions that use machine learning algorithms trained on large datasets to forecast how drugs might interact in the human body, helping healthcare providers avoid dangerous combinations.
We create personalized drug interaction prediction systems that integrate chemical, pharmacological, and patient data. By analyzing known interactions and the individual conditions of the patient, we provide healthcare providers with valuable insights that help prevent harmful effects from drug combinations.

Benefits:

  • AI-based drug interaction predictions: Predicting harmful interactions based on chemical properties, biological effects, and individual patient characteristics.
  • Real-time alerts for healthcare providers: Automatic detection of dangerous combinations during prescription creation or modification.
  • Data integration from multiple sources: Access to integrated data from chemistry, biology, and patient history for more accurate predictions.
  • Personalized recommendations: Tailored drug selection based on individual patient characteristics, optimizing treatment safety.

ROI impact:

  • Reduction of adverse drug events by 25–40%: Early prediction of interactions helps prevent harmful side effects and hospitalizations.
  • Improvement in prescribing accuracy by 20–30%: Healthcare providers receive reliable insights, reducing human error when selecting drugs.
  • Increased safety and patient satisfaction: Reducing the risks of harmful drug interactions enhances patient trust in healthcare services.

Challenges:

  • Conducting physical experiments with new molecules or components is expensive and time-consuming.
  • Limited number of experimental variations makes it difficult to select the optimal testing scenario.
  • Decisions are often based on a small number of trials, which do not account for all variables.

Solution:

Custom AI-Powered Data Insight Platform

A custom AI-powered data analysis platform that uses artificial intelligence to identify correlations, patterns, and hidden dependencies in your historical experimental data. This enables better understanding of processes, optimization of bioprocess experimental options, and more informed decision-making without the need for costly physical testing.

Benefits:

  • Saves time and resources by reducing the number of physical experiments required.
  • Enhances decision-making through data analysis and AI algorithms.
  • Optimizes focus on the most promising experiments.
  • Scalable to handle large and complex datasets.

ROI impact:

  • Reduces experimental costs by 20-30%.
  • Cuts time spent on physical testing by 15-25%.
  • Improves accuracy of predictions and quality of results.

Challenges:

  • Unstructured and Complex Data: Clinical trial data is large and complex. Without proper analysis, it’s difficult to identify useful patterns for drawing conclusions.
  • Overwhelming Volume of Data: The large volume of data makes manual processing difficult, increasing analysis time and reducing the ability to identify insights effectively.
  • Data Integration and Pattern Recognition: Integrating data from various sources to build a unified view of drug efficacy is challenging.

Solution:

Implement an AI-powered solution that integrates clinical trial data from diverse sources, such as patient demographics, biomarkers, medical histories, treatment protocols, and clinical outcomes. The system uses machine learning algorithms to analyze historical and ongoing data, automatically recognizing patterns within different patient groups and predicting treatment outcomes. It combines data from multiple clinics and sources into a unified platform, enabling a comprehensive view of the trial, improving data integration and correlation. This allows for faster, more accurate analysis, enhances decision-making by providing visual insights, and ultimately leads to more informed choices regarding drug efficacy and patient safety.

Benefits:

  • Faster Data Processing: Automated data processing speeds up decision-making.
  • Improved Pattern Detection: Identifying correlations between treatment efficacy and patient demographics.
  • Enhanced Decision-Making: Visualizations and analytics make it easier to make informed decisions regarding drug safety and efficacy.
  • Integrated Data Sources: Combining data from multiple clinics provides a comprehensive view of the trial.

ROI impact:

  • 25-30% Faster Analysis: Faster data analysis reduces time to decision-making.
  • 15-20% Reduction in Costs: Lower costs in data processing thanks to automation.
  • Increased Success Rates: Identifying effective patterns earlier improves drug development success.
  • Reduced Risk of Adverse Events: Early identification of risks decreases the likelihood of side effects.

Challenges:

  • Version Control Issues: In large research teams, tracking the latest version of documents is difficult, leading to the use of outdated files and compromising data integrity.
  • Fragmented Document Storage: Documentation is often spread across disconnected systems, making search and access time-consuming—especially critical during audits or regulatory reviews.
  • Data Privacy Risks: Poor security practices can result in leaks of sensitive patient or study data, leading to legal and reputational consequences.
  • Lack of Visibility into Regulatory Changes: Tracking updates from global regulators is a manual, error-prone process. Failure to react to changes quickly can result in non-compliance and costly remediation.

Solution:

Custom AI-Driven Regulatory Documentation Management 

We propose a custom AI-assisted Regulatory Intelligence and Document Management System designed to support your team in managing compliance throughout the entire research lifecycle. The system helps monitor global regulatory sources and quickly identify data relevant to new or updated requirements. Once updates are detected, it assists in locating associated internal documents and processes that may need to be revised. The system streamlines document navigation, highlights what requires review or re-approval, and recommends next steps based on predefined workflows.

Benefits:

  • Smart Document Linking: Automatically connects regulatory changes to related SOPs, documents, or trial stages requiring attention.
  • Centralized Document Access: A unified, searchable, version-controlled repository ensures easy access to all relevant documentation.
  • Data Privacy & Security: Compliant with standards like 21 CFR Part 11 and GDPR, providing secure handling of sensitive data.
  • Integrated Collaboration Tools: Role-based access, annotations, and change tracking simplify both internal reviews and external regulatory audits.
  • Reduced  manual workload, improves traceability, and keeps your team aligned with evolving regulatory expectations.

ROI impact:

  • 30–40% Reduction in Compliance Risks: Proactive tracking minimizes the likelihood of regulatory violations.
  • 20–25% Time Savings: Automation of mapping and version control streamlines compliance workflows.
  • 15–20% Cost Reduction: Fewer delays, fewer document errors, and less dependency on external consultants.
  • Improved Audit Readiness: All activities are logged, traceable, and audit-ready at any time.

Challenges:

  • Fragmented data storage systems: Departments involved in clinical trials, licensing, analytics, or formulation development often operate in siloed data environments. This leads to data duplication, loss of information, and difficulty in making informed decisions based on the full picture.
  • Delays in decision-making: Without centralized data access, collaboration between teams slows down, causing project delays and reducing overall research efficiency.
  • Reporting and analytics: Without a single source of truth, analytics become incomplete or inconsistent, limiting transparency of research results and complicating project performance tracking.

Solution:

Custom Integrated Data Management Platform

Break down departmental barriers by implementing a unified, automated data management platform that ensures seamless synchronization across all R&D departments and is fully tailored to your organizational structure and needs.

We design custom solutions that consolidate data from research, experimental, laboratory, preclinical, clinical, and analytical areas into one environment. This enables faster collaboration, more accurate analytics, and full regulatory compliance.

Benefits:

  • Centralized data access – A single control panel with role-based access
  • Real-time synchronization via API – Instant updates across all systems
  • Complete change traceability – Automatic logging of modifications and approvals to ensure compliance
  • Team collaboration tools – Shared reviewing, commenting, and data exchange across departments
  • Flexible integration – Adapts to both legacy and modern systems in your infrastructure

ROI impact:

  • 25–40% faster decision-making across teams
  • 30–50% reduction in data loss and duplication
  • Increased audit readiness and documentation quality
  • Enhanced cross-team collaboration throughout the research lifecycle

Challenges:

  • High Volume of Data Processing: Manual processing of large volumes of experimental data leads to delays and inconsistencies, making it challenging to manage and analyze real-time data effectively.
  • Limited Process Control: Traditional systems require manual efforts to maintain optimal conditions, leading to inefficiencies and a lack of adaptive capacity during experiments.
  • Slow Response to Changes: With manual tracking and data processing, it’s difficult to quickly adjust process variables or respond to unexpected results during experiments.

Solution:

Custom IoT Systems

Utilize an IoT-based solution to collect and analyze real-time data from lab equipment and biosensors. By integrating sensors that monitor critical variables such as pH, temperature, oxygen levels, and nutrient concentrations, this system allows researchers to track and adjust experimental conditions continuously. It provides actionable insights for optimized decision-making, automating responses to changing conditions and ensuring more efficient experiment management.

Benefits:

  • Accelerated Decision-Making: Real-time insights from IoT-connected devices enable quicker response to any variances in experimental conditions, improving the pace of decision-making.
  • Reduced Human Error: Automated data collection and analysis reduce the possibility of human error, enhancing the reliability and reproducibility of experiments.
  • Real-Time Monitoring & Adjustment: Instant access to real-time data enables immediate adjustments to process variables, reducing the need for manual oversight.

ROI impact:

  • 30% Reduction in Testing Costs: Real-time data and automated adjustments significantly reduce the need for physical testing and the associated costs.
  • 20% Faster Time-to-Market: Improved process efficiency accelerates the time required for drug development and regulatory approvals.
  • 10–15% Improvement in Experiment Success Rate: Better control over experimental conditions increases the success rate and consistency of results.
  • Resource Optimization: IoT-driven insights ensure more efficient use of resources, reducing waste and improving overall productivity.

Challenges:

  • Frequent Unplanned Downtime: Critical pharmaceutical equipment—such as peristaltic pumps, autoclaves, filtration systems, and sterilization units—often fail unexpectedly, halting GMP-compliant processes and causing batch losses or delays.
  • Reactive Maintenance Risks: Emergency fixes are not only costly but may also require re-validation of processes or environmental conditions, affecting FDA/EMA compliance.
  • Manual Inspections Are Ineffective: Periodic checks often miss subtle anomalies. Failures detected late can compromise aseptic conditions and product sterility.
  • Regulatory Pressure: Any equipment failure that affects validated processes requires deviation management and thorough documentation.

Solution:

AI Predictive Maintenance

We develop a custom-tailored predictive maintenance solution specifically for pharmaceutical manufacturing, where IIoT sensors are installed on critical equipment like compressors, cleanroom systems, and sterilization units to monitor parameters such as temperature, vibration, and pressure in real time. This data feeds into an analytics engine trained on typical equipment behavior in regulated production environments, enabling early detection of potential issues and forecasting failures 7–14 days in advance. Maintenance can then be scheduled proactively during planned downtime. The solution integrates with existing production and quality systems, automatically triggering service tasks and ensuring traceable, audit-ready maintenance workflows.

Benefits:

  • Up to 40% reduction in unplanned downtime, minimizing batch rework and release delays.
  • 15% savings in annual maintenance costs, through proactive servicing and fewer emergency repairs.
  • Improved regulatory compliance, with real-time documentation, deviation prevention, and full traceability.
  • Reduced need for revalidation, as equipment is maintained before failure, preserving process integrity.
  • Better OEE and resource utilization, ensuring manufacturing continuity and audit-readiness.

ROI impact:

  • 40% fewer production interruptions due to equipment failure
  • Up to 20% decrease in batch rejection rates due to environmental deviations
  • 30% improvement in maintenance scheduling efficiency
  • Stronger compliance posture for FDA, EMA, and MHRA audits

Challenges:

  • Visual Defects and Contamination: Tablets, capsules, and vials can exhibit chips, cracks, discoloration, or foreign-particle contamination that traditional manual inspection often misses or flags too late in the line.
  • Label and Print Verification: Misprints in batch numbers, expiration dates, or barcodes lead to recalled batches and compliance violations; manual OCR/OCV checks are slow and error-prone under high-speed conditions.
  • Fill Level and Seal Integrity: Under- or over-filled vials and improperly crimped seals risk dosage errors and microbial ingress. Manual gauging or random sampling fails to guarantee 100 % inspection coverage.
  • Throughput vs. Accuracy: High-speed production lines make it impossible for human inspectors to maintain > 99 % defect-detection accuracy without causing bottlenecks

Solution:

Custom Vision AI System

We develop a custom Vision AI system that seamlessly integrates with existing pharmaceutical production lines to enable real-time quality control. The system automatically detects shape defects, color inconsistencies, and foreign particles in tablets, capsules, and vials; verifies label accuracy, serial numbers, barcodes, and expiration dates without stopping the line; inspects liquid fill levels and cap integrity by comparing each unit to a reference image; and removes defective units from the line using robotic arms. All data is captured and securely stored to ensure full traceability and compliance.

Benefits:

  • Near-Perfect Defect Detection: Over 99 % accuracy in identifying visual defects and contamination, reducing escapes to downstream operations.
  • Full-Line Coverage: Automating inspection of every unit eliminates sampling gaps, ensuring consistent quality even at high throughput.
  • Regulatory Compliance: Automated audit trails and digital records of every inspection meet 21 CFR Part 11 traceability requirements.
  • Reduced Waste & Rework: Early removal of defective items lowers scrap rates  and minimizes costly batch recalls.
  • Operational Efficiency: AI-driven inspection reduces manual labor, reallocating staff to value-added tasks

ROI impact:

  • 15–25 % Reduction in Scrap Costs through accurate early defect removal.
  • 20–30 % Increase in Throughput by eliminating manual bottlenecks and inspections.
  • 10–15 % Decrease in Compliance Penalties by ensuring 100 % label and seal verification.
  • Rapid Payback: Most implementations achieve ROI within 6–12 months due to savings in labor, waste, and rework.

Challenges:

  • Manual Counting Limitations: Traditional mechanical or manual counting systems for vials, ampoules, capsules, or boxes are slow, error-prone, and often require line stoppages or human supervision. In high-speed pharmaceutical environments, even minor miscounts can impact batch integrity and inventory planning.
  • Lack of Real-Time Visibility: Inventory data is often updated post-factum, leading to delays in stock replenishment. Without real-time feedback from the production line, companies risk shortages of critical materials, causing production halts.
  • Disconnected Systems: Counting devices, production systems, and warehouse management platforms (WMS) are often isolated, making automated inventory reconciliation difficult. Manual data entry introduces errors and delays in synchronization.

Solution:

AI-Powered Inline Counting System

We implement vision-enabled inline counting systems that use advanced Vision AI to count pharmaceutical units — vials, ampoules, capsules, or packaging — in real time with <1% error margin. This eliminates the need for bulky traditional counters and reduces human involvement on the line. The counting data is instantly transmitted to the warehouse management system (WMS), allowing automated stock updates, triggering reorder workflows, and ensuring production continuity by preventing stockouts of critical components.

Benefits:

  • Automated Inventory Updates: Count data flows directly into the Warehouse Management System (WMS), enabling automatic inventory reconciliation and real-time stock visibility.
  • Faster Replenishment Cycles: Low-stock thresholds trigger automatic restock requests, minimizing downtime and preventing production delays due to missing materials.
  • Improved Operational Efficiency: With seamless integration between production and warehouse systems, the plant runs leaner and reacts faster to demand fluctuations.

ROI impact:

  • 80–90% Reduction in Manual Counting Time
  • >95% Inventory Accuracy Across Batches
  • 15–20% Reduction in Emergency Procurement Costs
  • Fewer Line Stops Due to Stockouts

Changes:

  • Large pharmaceutical sites face constant updates to FDA, EMA, and other global regulations, often requiring updates to documentation and requalification of processes, which can delay production.
  • Version Control Issues: Managing the latest versions of SOPs, batch records, and validation protocols across multiple shifts and facilities is error-prone. Using outdated documents can result in non-compliance during audits.
  • Fragmented Document Storage: Critical documentation, such as e-Batch Records and electronic signatures, is stored across different systems (QMS, LIMS, MES), making retrieval and search difficult, especially during audits.
  • Audit Readiness and Traceability: Manual or semi-automated processes for capturing audit trails and signatures create gaps in traceability. Demonstrating compliance with 21 CFR Part 11 often requires significant retrospective data collection.

Solution:

Custom Digital Compliance Platform

We create platforms that integrate with existing QMS, MES, LIMS, and ERP systems to automate regulatory change monitoring, document version control, and real-time audit-ready reporting. The platform continuously tracks global regulatory sources, flags relevant changes, and links them to affected SOPs, batch templates, and validation protocols, guiding experts through necessary updates. The e-Batch Records module automatically collects data from production lines, eliminating manual entry errors, and captures immutable audit trails and compliant electronic signatures.

Benefits:

  • Automated Regulatory Intelligence: AI monitors global regulatory updates, reducing manual tracking by up to 80%.
  • Centralized Version-Controlled Repository: A single searchable document store with version control and role-based access.
  • Seamless e-Batch Records & Audit Trails: Real-time capture of manufacturing data and signatures ensures traceability and batch release readiness.
  • Compliance Dashboard & Alerts: Live dashboards highlight regulatory deadlines and non-conformities, with automated alerts for timely reviews and re-approvals.

ROI impact:

  • 30-40% Reduction in Compliance Risk
  • 20-25% Time Savings in Document Management
  • 15-20% Cost Reduction in Compliance Operations
  • Improved Audit Readiness

Challenges:

  • High HVAC and Process Energy Loads: Pharmaceutical facilities allocate a significant share of energy to HVAC and steam systems, which often operate at fixed set-points regardless of real-time demand, leading to wasted energy.
  • Lack of Real-Time Visibility: Without continuous monitoring, inefficiencies like overuse of chillers or fans remain hidden, hindering targeted energy optimization.
  • Static Control Strategies: Traditional BMS systems rely on fixed schedules or manual tuning, missing dynamic adjustments based on production needs or ambient conditions.
  • Siloed Data and Limited Integration: Disconnected energy, MES, and BMS data prevent comprehensive analysis of how batch operations and shift activities affect energy consumption.

Solution:

Custom  Smart Energy Management Platform

We develop smart energy management platforms that integrate with existing BMS, MES, ERP, and IoT infrastructures to enable real-time monitoring, automated control, and predictive energy optimization. The platform continuously collects data from HVAC systems, chillers, and process equipment via wireless sensors, analyzing it with AI algorithms to detect inefficiencies, predict performance degradation, and recommend optimal operating parameters based on production schedules and environmental conditions. Interactive dashboards provide instant visibility into energy consumption per batch or shift, while automated alerts notify teams about abnormal patterns or required maintenance, helping pharmaceutical plants reduce energy costs and improve sustainability.

Benefits:

  • 10–15% Overall Energy Savings by minimizing idle time and optimizing schedules
  • 30% Reduction in Unplanned Downtime through predictive maintenance alerts
  • Shift- and Batch-Level Transparency for energy consumption, enabling accountability and continuous improvement
  • Automated Sustainability Reporting for GHG compliance with minimal manual effort

ROI impact:

  • Up to 14% Energy Cost Reduction in the first six months
  • 20–25% Lower Maintenance Costs by moving from reactive to predictive maintenance
  • 5–10% Production Throughput Gain from stabilized temperature control and reclaimed runtime
  • Accelerated Payback Period

Challenges:

  • Rising Disposal Costs: Large-scale pharmaceutical manufacturing and pilot lines generate substantial volumes of hazardous and chemical waste. Disposal costs are increasing annually, putting pressure on operational budgets.
  • Manual Classification & Tracking: Waste stream logging and categorization are often manual, time-consuming, and error-prone, leading to misclassification and the risk of regulatory penalties.
  • Regulatory Reporting Burden: Environmental compliance requires detailed waste manifests and regulatory submissions. Fragmented systems delay reporting and increase the risk of audit issues.
  • Sustainability & Packaging Waste: Pharma packaging contributes significantly to plastic waste, raising both environmental and reputational concerns.

Solution:

Custom Digital Waste Management Platform

We build purpose-driven waste management modules that integrate with MES, LIMS, and ERP systems to automate the classification, tracking, and reporting of pharmaceutical waste. Real-time production and lab data feed into the platform to categorize waste accurately, auto-generate compliant documentation, and provide centralized dashboards for audit readiness. Integrated sustainability analytics track packaging use and support the shift toward recyclable and biodegradable alternatives.

Benefits:

  • Up to 18% Reduction in Disposal Costs: Optimized waste categorization avoids excessive hazardous waste fees.
  • 50% Faster Audit Preparation: Centralized, immutable waste records and automated reports streamline audit readiness.
  • 25% Less Plastic Waste Over 3 Years: Sustainability tools drive packaging material changes, cutting plastic usage significantly.
  • Improved Environmental Compliance: Real-time alerts for storage duration, inspections, and non-compliance events reduce risks.

ROI impact:

  • 18% Waste Disposal Cost Savings
  • 30% Reduction in Manual Processing Time
  • 20% Fewer Sustainability Audit Findings

Challenges:

  • Storage Conditions: Drugs, especially temperature-sensitive ones, require precise control of temperature and humidity during transportation and storage. Failure to maintain these conditions can lead to a loss of drug efficacy and regulatory violations.
  • Supply Chain Traceability: Lack of complete visibility at each stage of the supply chain can lead to errors or delays, especially in urgent situations such as disease outbreaks or changes in demand.
  • Regulatory Compliance: Compliance with regulatory bodies’ requirements for the storage and transportation of drugs requires accurate data and real-time access to documentation, which is challenging when using traditional systems.
  • Risk of Contamination and Counterfeiting: Drugs can be damaged or falsified during storage and transportation, jeopardizing patient safety.

Solution:

Custom IoT Platform for Drug Delivery Control

The development of an IoT platform that integrates with existing systems to track the storage conditions and transportation of drugs at every stage of the supply chain in real-time. Sensors are used to monitor temperature, humidity, and location, allowing for prompt responses to changes and ensuring compliance with storage conditions.

Benefits:

  • Real-Time Monitoring: IoT sensors provide continuous monitoring of temperature and humidity in transport containers and storage facilities, ensuring proper drug storage.
  • Complete Visibility: Full visibility of every stage of drug delivery with accurate time and location, enabling rapid response in case of issues.
  • Automated Notifications: The platform sends automatic notifications about any deviations from normal conditions (e.g., temperature exceeding acceptable limits), allowing for swift corrective actions.
  • Secure and Verified Delivery: The use of blockchain technology or cryptographic signatures ensures not only precise monitoring but also protection against drug falsification during transportation.

ROI impact:

  • Reduction in Drug Loss Due to Improper Storage Conditions by 30-40%.
  • Time Savings in Tracking and Documentation of Deliveries up to 30%.
  • Improved Regulatory Compliance

Challenges:

  • Detection and Management of Adverse Drug Effects: Difficulties in detecting and managing adverse drug reactions after drugs are released to the market.
  • Issues with Systematizing and Monitoring Drug Safety: Poor monitoring of the ongoing impact of drugs, particularly beyond the traditional clinical trial phases (Phase IV).
  • High Monitoring Costs: Increased costs for drug safety monitoring due to manual processes.
  • Risk of Unauthorized Use of Drugs: Potential for misuse of drugs and lack of oversight after their market release.

Solution:

Custom Pharmacovigilance System

An AI-powered platform for drug safety monitoring that uses data analytics collected from hospitals and consumer reports to identify adverse effects and enable quick responses.

Benefits:

  • Cost Reduction: Lower costs for safety monitoring by automating data analysis and reporting processes.
  • Improved Adverse Effect Detection: More accurate and faster detection of drug side effects.
  • Faster Response Times: Enhanced ability to quickly respond to emerging safety concerns.

ROI impact:

  • 15% Reduction in Safety Monitoring Costs.
  • 25% Decrease in Adverse Drug Effects.
  • 30% Improvement in Response Time.

Challenges:

  • Low Medication Consumption Promotion: Pharmaceutical companies often struggle with encouraging patients to consistently consume their medications. This issue may stem from patients forgetting doses, lack of understanding of medication importance, or insufficient engagement with the prescribed regimen.
  • Non-adherence to Treatment Regimens: Patients often fail to follow prescribed treatment plans, which impacts the effectiveness of their treatment.
  • Ineffective Treatment Outcomes: Treatment effectiveness decreases when patients do not receive consistent support or consultations.
  • High Costs of Physical Monitoring: Continuous physical monitoring of patient adherence can be expensive and resource-intensive.
  • Challenges in Managing Treatment Programs: Difficulty coordinating and managing individual treatment plans leads to inconsistencies in patient care.

Solution:

Custom Patient Support System 

A mobile app-based solution utilizing AI to support patients in adhering to their treatment plans. Such  platforms offer companies an effective tool for promoting both existing and new products to their patient base. It provides personalized recommendations and reminders tailored to specific conditions, helping patients track their medication intake, adjust dosages, and monitor their treatment progress. Patients can upload their test results and progress, allowing for more informed treatment adjustments.

Benefits:

  • Real-Time Data and Insights: The platform provides pharmaceutical companies with real-time data on medication adherence, patient progress, and treatment outcomes, offering valuable insights for product development, marketing strategies, and regulatory compliance
  • Enhanced Product Promotion: The mobile app allows seamlessly promote both new and existing products.
  • Increased Adherence: By offering patients personalized AI-driven reminders, dosage adjustments, and continuous progress tracking, pharmaceutical companies can ensure higher adherence rates to prescribed treatments, ultimately improving treatment outcomes and increasing patient retention.

ROI impact:

  • Increased Product Sales up to 30%
  • Reduction in Operational Costs by 20%
    Increased Patient Retention by 20-25%
Unlock the ROI of pharmaceutical
digital transformation.
Anastasia Hula

VP of Customer Success in Healthcare

Anastasia Hula

CUSTOM PHARMACEUTICAL SOFTWARE SOLUTIONS WE BUILD

01. Drug Lifecycle Management Software

We build end-to-end platforms to manage every phase of the drug lifecycle, from R&D and clinical trials to regulatory approval and post-market surveillance, ensuring traceability, compliance, and speed to market.

02. Pharmaceutical Manufacturing Execution Systems (MES)

Computools develops MES solutions tailored for pharmaceutical production, enabling batch tracking, quality control, GMP compliance, and real-time monitoring of equipment and workflows.

03. Supply Chain and Cold Chain Management Software

Our custom platforms optimize pharmaceutical logistics with real-time tracking, temperature monitoring, and smart routing to maintain product integrity and ensure regulatory compliance during transport and storage.

04. Clinical Trial Management Software (CTMS)

We develop custom CTMS platforms for protocol tracking, patient enrollment, site management, and regulatory submission, improving trial transparency and shortening time-to-market for new drugs.

05. Pharmaceutical CRM and Salesforce Automation

We create CRM tools for pharma reps and distributors with territory management, HCP profiling, campaign automation, and integrated reporting, driving sales and improving HCP engagement.

06. Pharmacovigilance and Adverse Event Reporting Systems

Our platforms support real-time adverse event collection, signal detection, and automated reporting to regulatory bodies, helping pharmaceutical companies maintain drug safety and compliance worldwide.

Digitize your pharmaceutical operations today.

CHOOSE HOW YOU WORK WITH US

Consulting

Computools’ IT consulting services offer businesses the expertise to navigate complex digital challenges, ensuring strategic alignment with long-term objectives. We focus on delivering targeted solutions that optimize processes, reduce overhead, and position businesses for data-driven growth.

Software Engineering

Computools’ software engineering services deliver high-performance, custom software solutions for diverse business needs, ensuring seamless integration across platforms. Whether you’re in retail, finance, or logistics, we help businesses reduce operational costs and accelerate growth with a precision-engineered approach.

Dedicated Teams

Our dedicated teams empower businesses by providing skilled IT professionals who integrate directly into your project. With flexible support, we help speed up software project delivery, and drive innovation to keep your business ahead in a competitive landscape.

CX Strategy & Design

We optimize customer journeys to increase engagement, loyalty, and revenue through expert customer experience strategy and data-driven design.

Process Automation

Our automation solutions eliminate manual inefficiencies, optimize resource use, and reduce errors—driving faster, smarter business operations.

Software Modernization

We modernize legacy systems to boost performance, security, and flexibility—helping businesses reduce technical debt and seize new growth opportunities.

EXPERTISE ACROSS THE PHARMACEUTICAL TECH ECOSYSTEM

01 / 06
Clinical Trials Management Ecosystems (CTMS)
Laboratory Information Management Systems (LIMS)
Pharmaceutical Supply Chain & Serialization Ecosystems
Regulatory Compliance & Quality Management Ecosystems
Drug Discovery & R&D Data Platforms
Pharmacovigilance & Safety Monitoring Ecosystems

Medidata Rave Clinical Cloud

Oracle Siebel CTMS

Veeva Vault Clinical Suite

IBM Clinical

Development Medrio

Thermo Fisher SampleManager LIMS

LabWare LIMS

STARLIMS (Abbott)

Benchling

SAP ATTP

TraceLink Digital Network Platform

Optel Pharma Serialization Suite

MasterControl Quality Excellence

Veeva Vault QMS

Sparta TrackWise Digital

AssurX Quality Management

Schrödinger Drug Discovery Suite

BIOVIA Discovery Studio (Dassault Systèmes)

ChemAxon Platform

PerkinElmer Signals Research Suite

Dotmatics

Oracle Argus Safety

ArisGlobal LifeSphere Safety

Veeva Vault Safety

Testimonials

DISCOVER WHY CLIENTS TRUST COMPUTOOLS
5.0
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Verified by Clutch

“The team was very friendly and had the highest level of competence, engagement, and project management.”

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Stanley McAllister
Manager, Project Delivery, Unisys
Country
USA
Industry
Software
Budget
$200,000 to $999,999
Type
Web Platform
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Node.js React PostgreSQL
5.0
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Verified by Clutch

“Computools predicted all possible points of our business growth and implemented them into the project.”

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Dr. Bert Carl Schindler
Regional Manager of Business Area, DEIN DENTAL
Country
Germany
Industry
Healthcare
Budget
$200,000 to $999,999
Type
Web Platform
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Java JPA Servlets jQuery AWS
5.0
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Verified by Clutch

“We were highly satisfied with their deep understanding of our fintech processes and their project management was really superb.”

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Renata Patiejūnaitė
Regional Director Europe, iPiD
Country
Luxembourg
Industry
Finance
Budget
$200,000 to $999,999
Type
Mobile App
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Flutter Java Spring
5.0
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Verified by Clutch

“Computools is a highly professional company with a skilled and responsive team. Their ability to propose valuable improvements and their dedication to the project made a significant difference.”

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Wolfgang Fuchs
Data Scientist & Product Manager, METOS by Pessl Instruments
Country
Austria
Industry
Agriculture
Budget
$200,000 to $999,999
Type
Web System
Duration
Ongoing
Team Size
10 Specialists
Tech stack
OpenCV TensorFlow React Node.js PostgreSQL Docker Jira Slack
5.0
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Verified by Clutch

“The most noteworthy value that stood out was their exceptional experience in developing AI software solutions.”

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Aaron Thompson
Program & Project Manager, NextCare Health Conference
Country
USA
Industry
Healthcare
Budget
$200,000 to $999,999
Type
Web Platform
Duration
Ongoing
Team Size
10 Specialists
Tech stack
.NET C# ASP.NET MVC AZURE ANGULAR
5.0
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Verified by Clutch

“We were deeply impressed with their technical expertise, transparency, and flexibility. The team was highly skilled, easy to work with, and always proactive in solving challenges.“

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Todd Jezierski
Create & Deploy, Nike
Country
USA
Industry
Retail
Budget
$200,000 to $999,999
Type
Web System
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Java Spring PostgreSQL Angular Redis Docker
5.0
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Verified by Clutch

“Computools offered non-standard solutions and maximized their investment in our business success.“

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Samuel Ok
Senior Product Manager, Aya Healthcare
Country
USA
Industry
Healthcare
Budget
$200,000 to $999,999
Type
Web System
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Node.js NestJS PostgreSQL React WebSocket Biometric SDK
5.0
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Verified by Clutch

“Our company is impressed by their client-first approach and deep niche expertise.”

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Brian Mascarenhas
Co-Founder & CTO, Reset
Country
USA
Industry
Finance
Budget
$200,000 to $999,999
Type
Web Platform
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Python Django PostgreSQL React Redux
5.0
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Verified by Clutch

“A very comfortable collaboration and clear communication on every stage of platform development and maintenance.”

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Claus-Peter Müller
Managing Director, NLB Lease&Go
Country
Austria
Industry
Finance
Budget
$50,000 to $199,999
Type
Web Platform
Duration
Ongoing
Team Size
5 Specialists
Tech stack
Java Spring Boot PostgreSQL Docker AWS GitLab CI
5.0
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Verified by Clutch

“After all these years, Computools never fails to arrive on time and with a quality that never ceases to amaze me. They work well as a team and are adaptable and communicative.”

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Ryan L.
VP of Sales, Western Canada at Fastfrate Group
Country
British Columbia
Industry
Logistics
Budget
$50,000 to $199,999
Type
Web and Mobile App System
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Node.js React Native PostgreSQL Docker GitLab CI AWS
5.0
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Verified by Clutch

“Within the first three months of its use, the designed program by Computools significantly reduced meter reading fraud by over 30%. Additionally, we saw a rise in operational effectiveness. Customer comments highlighted greater billing transparency and speedier service delivery, which contributed to an improvement in customer satisfaction levels.”

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Frank Lindberg
IT Project Manager, European Energy
Country
Denmark
Industry
Energy
Budget
$50,000 to $199,999
Type
Mobile App
Duration
Ongoing
Team Size
5 Specialists
Tech stack
iOS Android Python TensorFlow React Native AWS PostgreSQL
5.0
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Verified by Clutch

“They were professional, adapted to our short-notice needs, documented everything, and were transparent.”

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Ben James
CEO, Hunter Healthcare
Country
United Kingdom
Industry
Healthcare
Budget
$50,000 to $199,999
Type
Mobile App
Duration
12 months
Team Size
5 Specialists
Tech stack
iOS Android Kotlin Swift Firebase REST API GitHub Bitrise
5.0
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Verified by Clutch

“Thanks to Computools, we have seen a 15% growth in sales and a 40% boost in user satisfaction. Our image management has become more efficient, and our diagnostic capabilities have improved. Overall, the team has delivered a high-quality solution that meets our requirements.”

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Quamil Woods
Paralegal, Sonic Healthcare USA
Country
USA
Industry
Consumer Services
Budget
$200,000 to $999,999
Type
Web System
Duration
Ongoing
Team Size
5 Specialists
Tech stack
C++ Python MQTT AWS IoT Core PostgreSQL Scrum
5.0
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Verified by Clutch

“Computools has significantly improved our LMS. The team holds regular meetings and provides detailed project reports, keeping us well-informed. We communicate via email, and overall, everything has gone smoothly.”

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Milad Hawsho
CEO & Founder, Pixil
Country
Sweden
Industry
Retail
Budget
$50,000 to $199,999
Type
Web System
Duration
Ongoing
Team Size
5 Specialists
Tech stack
Java Spring Boot React TypeScript PostgreSQL Docker
5.0
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Verified by Clutch

“Computools worked closely with us to understand our challenges. They developed a platform that integrated seamlessly with our existing infrastructure and Automatic Identification Systems (AIS) to capture private vessel data.”

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Billy Stonerock
Branch Manager, National Trench Safety
Country
USA
Industry
Consumer Services
Budget
$200,000 to $999,999
Type
Web System
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Go gRPC React PostgreSQL Redis Docker
5.0
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Verified by Clutch

“Computools’ work has had a positive impact on the client’s business. The team is flexible and responsive to the client’s needs. Their expertise has been key to the project’s success. Overall, the engagement has been positive.”

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Kevin Smith
Chairman & CEO, Spine
Country
USA
Industry
Retail
Budget
$200,000 to $999,999
Type
Web Platform
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Go React Redux ClickHouse Kubernetes AWS
5.0
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Verified by Clutch

“Due to the platform’s use, the new products’ generated go-to-market timeline improves by 20%, cutting down on plan costs and, most importantly, enhancing the connection between the departments. The availability of near real-time information and the enhancement of the speed of decision-making are truly remarkable.”

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David Vinyard
COO, Global Retailers LLC
Country
USA
Industry
Retail
Budget
$200,000 to $999,999
Type
Web System
Duration
Ongoing
Team Size
5 Specialists
Tech stack
Java Spring Boot Angular Kafka MongoDB GitHub Actions
5.0
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“Thanks to Computools, we have successfully implemented our system and reduced the need for manual inspections. The team works in regular sprints and keeps us updated on progress. Their personalized approach, ability to listen, adapt, and continuously refine their methods are truly impressive.”

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Philip Hewlett
Sr Leader/COO/VP Operations, East West Railway Company
Country
United Kingdom
Industry
Logistics
Budget
$200,000 to $999,999
Type
Web System
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Vue.js Node.js MQTT InfluxDB Redis Docker
5.0
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“Computools’ team truly impressed us with their dedication to the project, their ability to adapt to our processes, and their exceptional hard skills. This allowed us to identify many risks in the initial development stages and address some gaps in our processes. Professionalism, contribution, and flexibility are what define Computools. Based on my experience, I strongly recommend Computools for Dedicated Delivery and outsourcing project services!”

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Tim Kett
Director Product at abl solutions GmbH
Country
Germany
Industry
Software
Budget
Confidential
Type
Web and Mobile App System
Duration
Ongoing
Team Size
5 Specialists
Tech stack
Java React Android iOS Machine Learning SDK
5.0
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“Computools has delivered a functional solution that helped us increase revenue fivefold, reduce costs, and boost productivity. The team efficiently manages tasks in Jira and keeps us updated through weekly calls. Their productive approach and strong work ethic truly stand out.”

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Todd Williams
CEO & Chief Strategy Officer, Ensign Street
Country
USA
Industry
Software
Budget
$200,000–$999,999
Type
Web and Mobile App System
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Java Angular Android iOS
5.0
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“Computools’ technical knowledge is impressive.They delivered the product on time, within the agreed budget, and fully aligned with our requirements.”

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Jurijs Ivolga
CEO, SIA Volunge
Country
Latvia
Industry
Software
Budget
$10,000–$49,999
Type
Web System
Duration
Ongoing
Team Size
5 Specialists
Tech stack
Java React PostgreSQL
5.0
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“Thanks to Computools’ efforts, we have seen compliance with deadlines and budget and team scalability as needed. The team has a confident project manager who delivers a professional and organized project. Moreover, Computools has quickly onboarded to the project and delivered fast results.”

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Tannon Mccaleb
Payment & FinTech, Fintech Executive Search Consultants
Country
USA
Industry
Software
Budget
$200,000–$999,999
Type
Web Platform
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Java React Redis
5.0
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“Thanks to the new solution, we’ve significantly reduced manual marketing workflows. Computools manages the project effectively, using Scrum methodology to execute tasks efficiently. Their problem-solving skills and ability to anticipate challenges set them apart from other providers.”

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Brian Lent
Chief Analytics & Data Officer, Auger
Country
USA
Industry
Software
Budget
$200,000 to $999,999
Type
Web Platform
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Java React Redis
5.0
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“Computools has successfully delivered everything as planned, adding value to the app. The team is highly approachable, tracks progress, and provides real-time updates via Slack. They maintain smooth communication through email and messaging apps, regardless of time zones.”

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Scott Priddy
Process Improvement, General Dynamics Information Technology
Country
USA
Industry
Software
Budget
$200,000–$999,999
Type
Mobile App
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Android iOS Kotlin Jetpack Compose Firebase
5.0
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“Thanks to Computools, we now have an app that integrates 2,000 users into a single platform, significantly reducing the time spent on data exchange between systems and applications. The team manages our collaboration effectively and quickly adapts to changes. Overall, our experience has been highly successful.”

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Michael Haupt
Engineering VP, Quandoo
Country
Germany
Industry
Software
Budget
$200,000–$999,999
Type
Web System
Duration
Ongoing
Team Size
5 Specialists
Tech stack
Angular Java PostgreSQL
5.0
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“Computools has been responsible for creating a novel database and front-end solution, incorporating both the development portal for digital standards and a modern shop for the sale of these standards. Throughout the course of the project, we have been consistently impressed by the professionalism exhibited by the Computools team, as well as their detailed understanding of our client’s processes. Their expertise, commitment to our objectives, and consistent delivery of high-quality work are notable aspects of their service.”

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Bill Holler
Chief Executive Officer TraCert OÜ
Country
Estonia
Industry
Aerospace and Defense
Budget
$200,000 to $999,999
Type
Web and Mobile App System
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Vue.js PHP MySQL iOS Android
5.0
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“Thanks to Computools, the client saw a 35% increase in daily active users and a 25% rise in user retention rates. The Android app also saw a 20% reduction in load times. User feedback indicated high user satisfaction; the feedback highlighted the product’s enhanced navigation and content linkage.”

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Yulia Kondratyuk
Former Head of Sales & Marketing at Stfalcon LLC
Country
Estonia
Industry
Software
Budget
Confidential
Type
Mobile App
Duration
Less than a month
Team Size
5 Specialists
Tech stack
Java Node.js Express.js Android Redis
5.0
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“They are some of the best software developers I ever had the privilege to work with. Among other skills, their project scope and time estimation are very good and when wrong will work around the clock to make the date especially if it has business consequences. Not only are they amazing software developers, but they are also great people to work with. I am in awe seeing their devotion.”

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Elad Schiller
Co-Founder and CTO at CASTOR
Country
Israel
Industry
Software
Budget
Confidential
Type
Web Platform
Duration
Ongoing
Team Size
5 Specialists
Tech stack
Angular .NET PostgreSQL Redis Docker
4.5
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“Computools was selected through an RFP process. They were shortlisted and selected from between 5 other suppliers. Computools has worked thoroughly and timely to solve all security issues and launch as agreed. Their expertise is impressive.”

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Mona Madbouly
Global Web Officer at British Council
Country
United Kingdom
Industry
Education Services
Budget
$10,000–$49,999
Type
Web Platform
Duration
Ongoing
Team Size
5 Specialists
Tech stack
WordPress PHP jQuery AWS
5.0
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“After analyzing our requirements, Computools outlined potential solutions and deadlines for each stage. They designed the user flows and defined the user personas. They built the platform infrastructure and oversaw its implementation. Once we finished development, we conducted usability tests to assess their submitted work. Computools led an organized, agile team that adapted to our evolving needs. They listened to our feedback and managed their time well throughout the project.”

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David Roberts
Founder at ReVerb
Country
USA
Industry
Media
Budget
$50,000–$199,999
Type
Web Platform
Duration
Less than a month
Team Size
5 Specialists
Tech stack
PHP Laravel Vue.js PostgreSQL Docker
5.0
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“The application perfectly meets the large-scale demands of the project, with the team creating an effective solution that works well and provides the required level of control. They were communicative, responsive, and proactive throughout the project, demonstrating their experience at all times.”

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Daniel Beasley
CTO at Healthi
Country
USA
Industry
Healthcare
Budget
$200,000 to $999,999
Type
Web Platform
Duration
12 months
Team Size
10 Specialists
Tech stack
Java Spring Boot PostgreSQL AWS Docker
5.0
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“The Computools team came to us with ideas, and that’s unusual. I’m satisfied that they gave us the right recommendations which are contemporary and relevant for today’s users. Because with other companies on previous projects, it was like pulling teeth to get them to make suggestions. The product received positive feedback even before being implemented and has led to significant customer and revenue growth.”

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Greg Wilson
Chief Executive Officer at Herschel Supply Co
Country
USA
Industry
Retail
Budget
$1,000,000–$9,999,999
Type
Web Platform
Duration
12 months
Team Size
10 Specialists
Tech stack
React Node.js WebRTC PostgreSQL Docker
5.0
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“We had to meet a significant increase in the development, so we needed to scale up relatively quickly but cost-effectively. The result definitely meets our expectations. The completed project received positive feedback for features and overall design. They’re very organized from a project management perspective and they’re technically competent. We appreciated their innovativeness, professionalism, and great communication skills. ”

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Joshua Jimenez
CTO at Finna
Country
USA
Industry
Software
Budget
$50,000 to $199,999
Type
Web System
Duration
Less than a month
Team Size
5 Specialists
Tech stack
React Node.js PostgreSQL WebSockets
5.0
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“Their team has given us strong learning opportunities, and their developers are accommodating and collaborative.”

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Rusell Shumate
owner of data acess company
Country
USA
Industry
Software
Budget
$10,000–$49,999
Type
Mobile App
Duration
Ongoing
Team Size
4 Specialists
Tech stack
iOS Android Node.js Express.js Firebase Stripe
5.0
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Verified by Clutch

“We’re satisfied with the quality of work Computools deliver. They listen and try to understand our needs instead of finding new ways to charge us. We appreciate their transparent work structure. They kept us up-to-date regarding their progress throughout the entire development cycle. Knowing the system’s status throughout the coding process put my mind at ease.”

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Brian Hunt
CTO at Pich LLC
Country
USA
Industry
Logistics
Budget
$50,000 to $199,999
Type
Web System
Duration
Ongoing
Team Size
10 Specialists
Tech stack
Java Spring Boot PostgreSQL AWS Docker
5.0
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“They are very accommodating. They have very talented people. I’ve worked with hundreds of overseas developers and it’s not normal to have such excellent overseas developers. I don’t have to babysit Computools. They speak great English. They’ve also really helped with making suggestions on how to improve the product.
When we first launched our product at the beginning of the year, we were at 30,000 users a month and now we’re at 70,000. The bump in users is a result of the increased option rate and the new toys that Computoolls have built for me.”

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Jeremy Brown
CTO at SaaS world
Country
USA
Industry
Software
Budget
$200,000 to $999,999
Type
Web System
Duration
Ongoing
Team Size
12 Specialists
Tech stack
Node.js PHP React
5.0
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“Computools developed software for our business to help automate our processes. Their team is very easy to speak to over Skype, where I can speak directly to a designated client manager, project manager, and the development team.”

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DAVID HUMPSTON
Director Viewpoint Videos
Country
United Kingdom
Industry
Media
Budget
Type
Duration
Ongoing
Team Size
Specialists
Tech stack
5.0
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“They were able to reduce the customer entry acquisition process from 2–3 weeks to 48 hours and have completely optimized all business processes. They’re a trustworthy company, full of integrity and great principles. They also communicate well in spite of the distance and resolve problems quickly.”

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SIMON RICKETT
CEO Convertz
Country
United Kingdom
Industry
Capital Markets
Budget
$50,000 to $199,999
Type
Web Platform
Duration
24 months
Team Size
2 Specialists
Tech stack
PHP MySQL REST API jQuery Bootstrap
5.0
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“They have a very positive attitude, which I enjoy a lot, and their technical skills are impressive. During this project, I got acquainted with their VP in charge of technical development, and he’s very impressive. Technologically, they are on the cutting edge of what they do. They use a lot of interesting technologies, which is good.”

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Timothy Henderson
CEO at ViLand
Country
USA
Industry
Software
Budget
$10,000–$49,999
Type
Web System
Duration
Ongoing
Team Size
2 Specialists
Tech stack
PHP Symfony MySQL JavaScript jQuery Bootstrap

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