Data Intelligence & AI
Data intelligence in pharma IT needs more than large models. It needs data that is clean, complete and ALCOA+-compliant — and the ability to merge fragmented sources into a single analysis-ready foundation. We combine classical data science disciplines with modern AI architecture, for applications that not only work but also stand up to compliance.
Data and AI capabilities at a glance
Data intelligence begins with the data foundation, not the model
Data Intelligence and AI refers to the layer between raw data sources and analytical applications: data engineering, statistical modelling, visualisation and finally AI components such as RAG architectures or LLM integrations. In life sciences, the value of this layer is determined less by model size than by the quality of the data foundation. Data that sits fragmented across countless systems — from ELN through CTMS to manufacturing data — must first be merged into a consistent, ALCOA+-compliant basis before reliable analytics or productive AI can build on it. That is exactly where we come in: at data architecture, platform choice (Lakehouse, Microsoft Fabric, Databricks) and at end-to-end integration into the regulated pharma IT landscape.
Four disciplines along the data stack
From data source to productive AI application — each layer with the discipline that regulated pharma data demands.
Data Visualization & Dashboards
Power BI is the backbone of the visualisation layer in pharma-typical stacks. We design, build and operate dashboards for clinical trial data, sales performance, lab data or production KPIs — including training and adoption programmes for business teams. Where Power BI does not suffice, alternative BI stacks or custom dashboards come into play.
Data Science & Analytics
Classical data science work from exploratory analysis to production pipelines. We work in Python and R, on Databricks, Microsoft Fabric and Apache Spark — with an understanding of the regulatory constraints that must run alongside pharma analytics. Data validation, reproducibility and traceable model development are part of the discipline.
Data Integration & Migration
The hardest work in the data stack is not the model, it is the source. We build ingest pipelines for lakehouse systems, integrate line-of-business systems through MuleSoft and REST interfaces, and steer data migrations across all dimensions — from SharePoint to O365, tenant to tenant, legacy document management to Veeva Qdocs. With the focus that audit trails and data integrity do not get lost along the way.
AI Frameworks & RAG Architectures
Generative AI becomes productive when models can draw on well-structured proprietary data. We build RAG pipelines, vector stores and LangGraph-based agents that bring life-science-specific knowledge into language models — with compliance mechanisms such as audit logs, source traceability and controlled data flows, so that AI remains usable in regulated contexts.
Data as the connecting layer across the pillars
Data does not come out of nowhere — it originates in lab, clinical and platform systems, runs through validation requirements and must flow into productive applications. Where our data work most directly meets the other pharma IT disciplines.
Data platforms and AI frameworks
We work with the platforms and frameworks established in the pharma data stack — from lakehouse systems to modern AI stacks.
Smarter data, robust AI
From data architecture sparring to a productive AI application — we bring the experience to combine both disciplines in the regulated pharma context.