Built for regulated-industry engineering
The systems that matter in pharma and life sciences (clinical trial management, real-world evidence platforms, pharmacovigilance, regulatory submissions, manufacturing execution, and commercial analytics) share the same engineering DNA: validated SDLC, auditable data pipelines, model explainability, and the operational discipline to deliver inside FDA, EMA, and HIPAA constraints. These are not new problems for us. We've spent 29 years inside regulated-industry IT, and our airisDATA practice has spent over a decade shipping production AI inside tier-1 banks where the regulatory and explainability requirements are equally serious.
We bring the engineering and the regulated-industry discipline. You bring the scientific and clinical domain. We partner with your SMEs to build the systems.
AI for Drug Development, Formulation, and Regulatory
Accelerate the pipeline from molecule to market
The 2025-2026 pharma landscape has been reshaped by two converging shifts: generative AI is moving from early discovery into formulation, clinical operations, and regulatory submission preparation, and the regulatory environment around it has hardened. The FDA's January 2025 draft guidance on AI for regulatory decision-making and the joint FDA-EMA guiding principles released in January 2026 have established a higher bar for model validation, biological credibility, and human oversight. Drug developers that move first in this environment need engineering partners who can ship AI that survives a regulator's scrutiny, not just AI that demos well.
That is the airisDATA pattern transferred into pharma. The same model explainability frameworks (XAI), validated training pipelines, audit-ready model registries, and data-quality automation we've shipped into tier-1 banks for SR 11-7 model risk management map directly to FDA-aligned model governance. The same NLP and transformer architecture behind our automated contract review system applies to clinical study report drafting and regulatory submission assembly.
AI for formulation and process development
- Stability prediction and shelf-life modelling
- Excipient compatibility analysis
- Process risk assessment and design-space optimisation
- Generative AI for personalised formulation
AI for drug development and clinical operations
- Patient cohort identification from EHR and claims data
- Trial site selection and recruitment optimisation
- Dropout risk prediction
- Real-world evidence (RWE) data engineering for regulatory submissions
- Generative AI for de novo molecule design support workflows
AI for regulatory affairs
- Clinical study report drafting
- eCTD regulatory submission assembly
- Labelling and structured product labelling automation
- Pharmacovigilance signal detection (NLP and anomaly detection)
- Post-market surveillance and real-world data analytics
Validated AI engineering and model governance
- Model validation and governance frameworks
- Explainable AI (XAI) for regulated decisions
- Audit-ready model registries and lineage
- FAIR data implementation (findable, accessible, interoperable, reusable)
- Bias detection and human-oversight workflow design
Data Engineering and AI Platforms
Collect, clean, govern, and activate your data
Manage end-to-end pharma data pipelines with CI/CD workflows, IaC infrastructure, advanced data quality automation, and stream processing.
Analytics platform
Implement a robust, cloud-native analytics platform with state-of-the-art data processing and multimodal AI models that support FDA- and HIPAA-compliant genomic and patient data analytics, clinical trial insights to support drug discovery, commercial recommendations for sales engagement, and IoT data for personalised patient experience and remote monitoring. Our airisDATA Finance Data Hub architecture (multi-tenant, role-based, audit-ready) adapts directly to clinical, commercial, and manufacturing data.
- Analytics platform engineering
- Stream processing and real-time data
- Data quality automation
- Data governance and lineage
Machine learning platform
Integrate an ML platform with MLOps and DataOps processes to improve data accessibility and quality, accelerate insights, and increase business impact. We bring the same MLOps patterns we've shipped into tier-1 banks (model explainability XAI, validated training pipelines, and audit-ready model registries) into the pharma context.
- ML platform engineering
- MLOps and model governance
- Model explainability for regulated AI
Experience Engineering
Reimagine the digital experience for patients, HCPs, and commercial teams
Design experiences around the changing needs of patients, caregivers, and HCPs across complex care journeys. Introduce new engagement approaches for commercial teams. Build end-to-end omnichannel experiences optimised for personalisation and convenience.
Commercial engagement and Next Best Action
Augment sales and marketing operations with advanced analytics and AI. Develop a digital personalisation strategy that uses Next Best Action to optimise HCP engagement. Gain a deep understanding of HCP behaviours, preferences, and needs with historical and real-time insights and recommendations.
- Next Best Action recommendation engines
- HCP segmentation and engagement analytics
- Marketing technology and omnichannel orchestration
Patient-first services
Empower patient-support specialists and HCPs with advanced analytics, end-to-end connectivity, and access to insights that improve patient compliance and engagement. Strengthen remote patient monitoring through patient portals and decision-support tools that integrate data from mobile, wearables, and medical sensors.
- Patient engagement and support platforms
- Remote patient monitoring software
- Conversational AI for patient experience
Digital commerce for life sciences
Digitise the end-to-end commerce journey for B2B buyers and B2C patient and HCP audiences with composable commerce architecture. Best-in-class components for pricing, order management, supply chain optimisation, personalised search and recommendations, and order fulfilment.
- Composable commerce platform engineering
- B2B portal engineering
- Order and inventory management integration
Operations Efficiency and Cost-Saving
Optimise the pharma and life sciences value chain
Supply chain optimisation
- Supply chain visibility platforms
- Demand forecasting and inventory analytics
- Cold-chain monitoring and serialisation
Smart manufacturing
- Predictive maintenance and equipment health monitoring
- Visual quality inspection and computer vision
- IoT and edge analytics
Research and the product lifecycle
- Clinical trial management system engineering
- Real-world evidence (RWE) data engineering
- GxP-aligned validated SDLC
- Integration across discovery, clinical, and regulatory data sources
Why Innovative for pharma and life sciences
- 29 years in regulated-industry IT services
- 12 years of production AI inside tier-1 banks through our airisDATA practice. The same explainability, data-quality, and validated-pipeline patterns transfer directly to GxP environments.
- 150+ engineers across Princeton, Hyderabad, and Pune
- Hybrid onshore-offshore with onshore architects in Princeton, NJ
- Strategic partnerships with AWS, Microsoft Azure, Google Cloud, Snowflake, Databricks
- WBENC-certified MWBE. Qualifies for diversity-spend programmes at every major pharma manufacturer and CRO