Data engineering built for Life Sciences
We help pharma and biotech teams connect HCP, clinical and research data – and put it to work in reporting and AI. From more reliable reporting to easier access to scientific and medical knowledge, we support the work from architecture through implementation and ongoing development – as part of our Life Sciences practice.
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75%+of our business is in Life Sciences
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15+years delivering enterprise data projects
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500+engineers and consultants
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400+IT projects delivered
CRM, clinical and enterprise systems each hold part of the picture. Combining them is hard – even when a technical connection already exists.
Signs we often see:
- Different versions of the same information across systems
- Data preparation that starts again for every report or model
- AI pilots that don’t scale beyond selected documents
- Cloud costs without a clear view of what drives them
Left: the reference architecture we adapt to your existing stack.
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Build data flows between business applications, databases and document repositories. We develop integrations and pipelines that bring information into a consistent structure – including master data for HCPs, accounts and products – for reporting and further processing, reducing repeated manual preparation. For example: HCP data from CRM into reporting, or clinical and research data into a shared lakehouse.
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Identify where the current architecture limits performance or makes new requirements expensive to deliver. We design and implement changes ranging from targeted improvements to migrations from legacy data warehouses and Hadoop environments to modern cloud platforms.
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Prepare data for analytical applications and machine learning models. We build ETL/ELT, batch and inference pipelines that handle complex business rules and demanding workloads, so teams can use their models without manually correcting the data at every run.
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Make knowledge from scientific literature, clinical study reports, SOPs and medical information easier to find and use. We build applications using retrieval-augmented generation, entity extraction and other AI components that return answers with links to the source documents, so experts can verify them – with testing and deployment processes that support ongoing development. Beyond RAG, we also support structured content authoring and metadata extraction and normalisation for document-heavy processes.
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Read the case study
Making research knowledge easier to find
Global healthcare company · R&D
An R&D team wanted to use information held across large document repositories. Craftware implemented RAG pipelines and a conversational interface on a Databricks-based platform, enabling researchers to ask questions in natural language. Automated testing and deployment supported further development of the solution.
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Read the case study
Bringing disconnected data into a shared lakehouse
Global consumer health company
Data was spread across multiple sources, including legacy and proprietary systems. Craftware built a Databricks lakehouse as a single source of truth, used AI-driven cataloging to organize master data and added a “talk to documents” capability for faster knowledge extraction.
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Read the case study
Building the pipelines behind a data-based product
Multinational healthcare organization · Clinical trials and market analysis
The client’s product required complex data preparation and demanding analytical workloads. Craftware developed batch and inference pipelines using Azure and Databricks, supporting the Data Science team while simplifying the integration of AI components.
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Read the case study
Connecting Vault CRM with downstream data systems
Global biopharmaceutical company · 100+ countries
Within a multi-vendor Vault CRM migration, Craftware connected Vault with legacy data stores and analytics systems through Vault API and Azure integrations, including data mapping, validation and post-go-live support.
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Agree what key datasets represent, who is responsible for them and how inconsistencies are resolved. Shared product, HCP or account information needs an agreed meaning when it moves between teams and systems.
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Commercial and HCP data is subject to GDPR and consent rules; patient-level data may need pseudonymisation before it reaches analytics or AI. These requirements are designed into the pipelines, not added after go-live.
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Define who can use which data and for what purpose. Record data lineage – its sources and transformations – in a data catalog, alongside the access controls and activity logs needed for the application. For GxP data, this supports data integrity expectations such as ALCOA+, audit trails, 21 CFR Part 11 and EU GMP Annex 11.
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For GxP use cases, we agree the validation scope and responsibilities with your quality team. Our risk-based approach draws on GAMP 5 and relevant Computer Software Assurance principles, with testing and evidence proportionate to the system’s intended use and risk. When new data or AI tools change how teams work, Change Management helps make sure they are adopted as intended.
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Define how users will check AI-generated results and where expert review is needed. The ISPE GAMP Guide: Artificial Intelligence and the draft EU GMP Annex 22 point to the same priorities: a defined intended use, independent test data, performance monitoring and human oversight. For scientific or medical information, review steps are part of the workflow before rollout.
Craftware combines data engineering and AI delivery with experience in the business applications that produce and use the data.
As a Databricks Partner, we build modern data platforms and integrate them with CRM and legacy systems. Our Salesforce and Veeva experience also supports projects where a platform change affects data flows and the applications around them.
Our project experience includes corporate data integration, pipelines for Data Science and AI applications supporting research teams. We can work on the data platform itself or on a defined part of the wider solution alongside your internal team.
We start from your current architecture. These are the platforms and technologies our data and AI teams work with most often.
Data platforms
DatabricksSnowflakeMicrosoft FabricGoogle BigQueryOraclePostgreSQL
Cloud
Microsoft AzureAWSGoogle Cloud
Integration & processing
Apache SparkAzure Event HubsInformaticaTalendVault API
AI & analytics
Azure OpenAIClaudePower BI
Delivery
GitHub ActionsAzure Container Apps
The right starting point depends on what is already in place and what your team needs to achieve.
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Assess the current setup
—Review of your stack, performance, maintenance effort and security
—Findings with prioritized recommendations
—A roadmap for the next changes
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Deliver a project
—Integration, platform migration or AI application
—End to end, or a defined part alongside your team
—A focused proof of concept where feasibility is uncertain
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Extend your team or solution
—Data engineers, architects and ML specialists
—Support that continues after go-live
—Ongoing optimization of pipelines and applications
HLS Account Manager
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No. Our data engineering work includes multiple source systems and data types. CRM may be one source, alongside enterprise applications, databases and document repositories.
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No. An assessment is useful when the current limitations or next steps need clarification. With a defined scope, we can move into delivery or provide expertise for a specific part of the project.
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Databricks is an important part of our expertise, but the starting point is your existing architecture and requirements. We also work with Snowflake, other cloud data platforms and on-premise environments.
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We test answers against evaluation sets built from your documents and questions before release, and we keep monitoring quality, usage, and cost after go-live. Changes go through automated tests in a CI/CD pipeline.
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Both. We can extend an existing warehouse, migrate it, for example, from Oracle or Hadoop or connect it to a lakehouse step by step.
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Agents need the same foundations as reporting or RAG: connected, well-defined data and clear access rules. Plus two more: permissions that limit which tools and data an agent can use, and an audit trail of what it did. We help put those in place before agents go live. See how AI agents can support Life Sciences teams →
