For biotech & pharma R&D teams in NL/EU
Biopharma R&D that survives real constraints.
Built for biotech & pharma teams in NL/EU, turning messy experimental reality into validated hypotheses and reliable systems. Progress compounds across studies, sites, and time.
- Fix the substrate: integration, lineage, quality gates.
- Make outputs trustworthy: evaluation that treats GenAI outputs as hypotheses, not facts.
- Make it deployable: traceability + oversight + robustness aligned with EU expectations.
EU-based. You leave with a concrete plan, not a pitch. Built for regulated environments (traceability, oversight, security).
Source: IDBS · NIST · EU AI Act · ISPE · Pistoia Alliance
What’s blocking R&D velocity (in 2026)
Five failure modes: they compound each other if left unaddressed.
"Data exists, but it's not AI-ready."
- ELN, LIMS, and CRO deliverables live in separate schemas: no lineage, no shared ontology, no integration layer.
- Scalability, flexibility, and data integrity bottlenecks delay every downstream analysis.
Every experiment starts with undocumented cleanup. Velocity bleeds before the model even runs.
"The model answers, then you can't defend them."
- GenAI confabulation is a documented, systematic risk: outputs sound plausible but can be wrong in non-obvious ways.
- Without structured evaluation against held-out ground truth, you cannot separate reliable signal from statistical noise.
Bias and unverifiable claims erode trust faster than they're built. Evaluation is not optional.
"POCs don't survive the organisation."
- EU high-risk AI requirements demand logging, traceability, human oversight, and robustness: concepts rarely designed into pilot notebooks.
- Reproducibility breaks when the environment changes. Change control is missing; monitoring is an afterthought.
Most AI R&D pilots die here. Reliability ≠ accuracy. It means surviving the organisation.
"New failure modes show up when you connect models to workflows."
- Data poisoning and prompt injection become live risks when AI is integrated into GxP-adjacent data flows: not theoretical ones.
- EU AI Act (high-risk, Aug 2026 enforcement) requires explicit cybersecurity and oversight provisions: gaps here create real regulatory exposure.
The security surface expands with every new integration. It needs explicit scope, not wishful thinking.
"Tools evolve monthly; teams can't keep pace."
- Three-quarters of life sciences labs expect AI use within two years: but skills shortages are a growing barrier to execution.
- Tool sprawl and ownership gaps mean momentum depends on one or two individuals rather than durable team capability.
Throughput bottlenecks compound. A skills gap today is a 12-month velocity gap in 18 months.
Three pillars
Reducing uncertainty, making
progress compound.
Outcomes, not activities. Each pillar is designed to reinforce the others.
Builder strategy
Outcome: a roadmap of tested hypotheses
- Identify high-impact bets.
- Define what 'working' means (KPIs + eval).
- Create a roadmap of tested hypotheses.
Why it matters: decisions made without a tested hypothesis roadmap are opinion-driven, not evidence-driven.
Engineering for reliability
Outcome: systems that survive the organisation
- Turn notebooks into reproducible pipelines.
- Add monitoring + change control.
- Reduce friction across the team.
Why it matters: reproducibility, traceability, and change control are necessary conditions for EU deployment readiness.
Model craft
Outcome: decisions under real constraints
- Model the process, not just the data.
- Quantify uncertainty , don't hide it.
- Optimize decisions under constraints.
Why it matters: a model that can't quantify what it doesn't know can't support decisions: it just adds false confidence.
Where the three pillars overlap
Decision-grade R&D system
Fixed scope · 2 weeks
Uncertainty Reduction Sprint
10 days. I audit your biopharma data reality, map regulatory constraints, design the validation experiments, and hand you a plan you can defend to stakeholders.
You leave with
- 1
Regulatory exposure map
Where your data gaps create EMA or EU AI Act risk.
- 2
Prioritized hypothesis map
What to test first, ranked by impact and data feasibility.
- 3
Evaluation protocol
What counts as working, with success criteria per hypothesis.
- 4
Pilot scope with risk gates
6-week roadmap structured for traceability and oversight.
- 5
Architecture sketch
Minimal reliable system — not a wishlist.
High uncertainty
Selected work
What it looks like in practice
Short and technical. The details that matter.
Bespoke modelling + optimisation under constraints
Named: Aimwel (joint venture with DPG Media + Randstad)
End-to-end: policy design, evaluation harness, and deployment pathway: built to be understood, monitored, and maintained.
+32% outcome at fixed budget
Marketplace setting with business constraints and real-time pressure.
Privacy-preserving, explainable clinical decision support
Named: GeriMedica: geriatric EHR platform
Signal extraction from clinical notes + workflow integration. Designed for trust: explainability, data minimisation, oversight hooks.
Pilot in progress
Also relevant for healthcare software teams (see /healthcare-software).
R&D data platform: large-scale annotation + sharing
Anonymised on request
System design, pipelines, and team enablement: built to support annotation workflows across studies at scale with shared lineage.
System operational
Details available under NDA during triage conversation.
EU AI Act · EMA · ISPE · Digital Strategy
Built for the Dutch/EU operating reality.
Not a compliance checklist. Builder-focused: constraints designed around from day one.
System compliance stack
Data lineage
Evaluation harness
Monitoring & alerting
Human oversight
Documentation & audit
Data lineage
Evaluation harness
Monitoring & alerting
Human oversight
Documentation & audit
Each layer is considered at design time, not patched in after delivery.
Before you decide
Frequently asked questions
R&D Triage · 30 minutes
Book the 30-min
R&D Triage.
No demo, no deck, no pitch. A structured conversation about your specific situation: and a framework for moving forward.
A tested-hypotheses roadmap direction
What success looks like in 6–10 weeks
What not to do (common traps for your situation)
4+
Production AI systems in life sciences & healthcare
30 min
Free, no pitch: useful regardless of next step
EU-based. Works with EU / Dutch teams.
Book your R&D Triage
Takes 2 minutes. You'll hear back within one business day.