Building data-driven software for the life science industry.

Helping organizations structure, process, and make use of their data.

• Custom Software Development: Purpose-built systems that align with regulatory and clinical requirements

• Data Tracking: Real-time insight into data flows and key operational indicators

• Data Visualization: Interactive dashboards for informed decision-making

• Analytics: Extracting value from complex datasets through statistical models

• Integration: Unifying data from disparate systems (e.g. EDC, EHR, LIMS)

Turn your concept into a working product - faster, safer, and with real-time validation.

• Product Feature Prioritization: Identify what to build first and why

• Data Validation: Make decisions backed by real-world data, not assumptions

• Prototype Design: Modular MVPs ready for testing and iteration

• User Feedback Loops: Constant iteration driven by user behavior and Continuous iteration through in-app analytics, user interviews, and sprint-based feedback cycles.

Building digital infrastructure with automated solutions and cloud-native expertise.

• Cloud Migration and Modernization to a more secure and cost-effective cloud infrastructure.

• Infrastructure as Code: Scalable, repeatable environments via automation.

• CI/CD Pipelines: Faster deployments with fewer errors.

• Monitoring & Performance: Full visibility into system health and uptime.

Practical AI tools that enhance—not replace—human decision-making. DataDrill delivers both the technical implementation and integration services, ensuring AI becomes a functional part of daily workflows in regulated environments.

• Practical AI tools that enhance: not replace—human decision-making. DataDrill delivers both the technical implementation and integration services, ensuring AI becomes a functional part of daily workflows in regulated environments.

• Natural Language Processing (NLP): Implementation of AI pipelines and applications that extract meaning from unstructured clinical and operational data.

• Predictive Analytics: Development and deployment of forecasting models that anticipate trends, behaviors, and outcomes in pharma and biotech, tailored to your historical and live data.

• Custom ML Models: End-to-end design, training, and validation of machine learning models based on your internal datasets and specific business objectives.

• AI-Powered Assistants: Building and integrating chatbot-like tools that assist users in repetitive tasks.

• Data Automation: Engineering intelligent workflows that reduce manual tasks by integrating AI into data pipelines, dashboards, and reporting tools.