Pharmaceutical Software Solutions

• 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.

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.

• 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

• 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

• 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.

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.

• 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

• 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

• 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.

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.

• 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.

• 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.

• 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.

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.

• 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.

• 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.

• 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.

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.

• 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.

• 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.

• 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.

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.

• 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.

• 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.

• 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.

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.

• 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.

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

• 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.

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.

• 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.

• 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.

• 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.

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.

• 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.

• 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.

• 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.

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.

• 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

• 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

• 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.

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.

• 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.

• 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.