Life Sciences & Health
Research, quality and care, with privacy and validation built in.
Precision matters more here than anywhere. We help pharma, biotech, medtech and healthcare organisations find signal in complex data and act on it.
- 01
Diagnostic modelling
ML models that identify patterns in genomic, imaging, and clinical datasets, supporting earlier diagnosis and treatment optimization where the stakes are highest.
- 02
Regulatory compliance
Data lineage, audit trails, and reporting workflows managed to standard, so life sciences teams meet regulatory requirements without burying themselves in manual processes.
- 03
Clinical data analytics
Platforms that consolidate, clean, and analyze clinical and patient data, providing a faster path from raw data to decision-making.
AI in production, story by story.
Pick one to see the result, the client behind it and the full story.
Use Case · R&D Data Platform~80Active UsersA life sciences leader puts six departments on one R&D platformA unified platform to accelerate collaboration, reduce duplication, and enable data-driven researchUndisclosed ClientFull success story 
Use Case · Connected Device InsightsMaven Pet pilots context-aware wellness alerts in a proof of conceptAdding intelligence to a pet wearable platform: from hardcoded alerts to contextual insights and owner conversationsFull success story 
20%better forecast accuracy than the manual method, validated against the October to December hard closeCUF forecasts hospital demand 20% more accurately than its manual methodDaily demand forecasts across every hospital unit, on their first ML platformFull success story Undisclosed Client~80Active Users20%better forecast accuracy than the manual method, validated against the October to December hard close
Questions
Talk to usHow do you handle sensitive health and research data?
Data stays inside the perimeter you choose, access rules decide who sees what, and the system stays within clear boundaries, for example no clinical advice.
What happens after go-live?
The team that builds your solution runs it. We monitor quality and adoption, fix what drifts and improve the solution as your business changes.
Where does our data live?
You decide. We deploy in our cloud, in your own cloud account or on your servers, and your data stays inside that perimeter under your rules.
Do we need an AI strategy before we start?
No. If you already have a use case with an owner, workable data and internal agreement, we can build it. If not, the AI Assessment finds and ranks the opportunities first.
Can you work with our own data and IT teams?
Yes. We adapt to your stack and work side by side with your engineers. We can also plan the move from a DareData team to your own team over time.
Which AI models do you use?
We are not tied to one provider. GenOS works with models such as Claude, GPT, Gemini, Mistral and Llama, and we pick per task for accuracy, cost and where the data may go.
Who owns what we build together?
Ownership is agreed in the contract before work starts. Ask us for our standard terms on the first call.
How is a project priced?
It depends on scope, length and the team it needs. After a first conversation we send a proposal fitted to your case, not a price list.
How do you keep our data secure?
Every solution runs on GenOS, with guardrails, access rules and a log of each answer, action and model call. Your security and risk owners review the design before anything goes live.
How soon do we see results?
The AI Assessment takes two working weeks and ends with a ranked plan. Most of our builds reach production in two to six months, depending on scope and data.
Other industries
Bring us your hardest AI problem.
Large enterprises trust us to carry AI all the way into production. We shape the use case, build it into your systems and run it with the governance your business needs.
- 01Use cases chosen for value, not hype
- 02Systems built into your stack and data
- 03Run, monitored and improved after go-live