Healthtech · Health Data & Interoperability · 200–1000 · Europe #b030_21

Context

We build AI systems for drug discovery and development, using a network of hospital research data that stays under each institution's control.

Industry

Healthtech · Health Data & Interoperability

Size

200–1000 employees

Region

Europe

Tools budget

$5K–20K per month

Needs

Data governance & catalog. We work with patient data from hundreds of hospitals and must prove every analysis respects each site's permissions.

Today: federated access where each hospital keeps control, with per-site permission checks on analyses · Urgency: now

Guardrails for AI agents and LLM apps. Our autonomous research agents act on sensitive clinical data, so we need strong controls on what they can access and output.

Today: agent deployment inside pharma partners' own infrastructure under multi-year licences · Urgency: now

Cloud cost optimization. Training and serving large models for drug research makes our compute bill one of our biggest costs.

Today: hyperscaler marketplace distribution and accelerated-compute partnerships for heavy model workloads · Urgency: this year

Who decides

cto · security review

Expects from suppliers

Strong controls for federated and sensitive data; Scales to heavy model training workloads; Experience with pharmaceutical quality expectations

Not a fit

Tools requiring central pooling of patient data; Consumer-grade AI services

Stack

cloud: AWS-class hyperscaler, LLM APIs in production, Kubernetes, data warehouse: cloud

Buying triggers

AI adoption push, cost pressure