Eliminate R&D uncertainty
with strategic AI development
you can operate and defend.
Built for biopharma and life sciences R&D: strategy, data pipelines, models, and infrastructure that survives EMA review, org change, and production reality.
Senior hands-on delivery · EMA-aware · EU AI Act–ready
Where the fragility lives
Five ways R&D AI breaks
Most teams hit one of these. Many hit several at once. Recognise yours?
Data foundations
Silos · weak metadata · brittle ETL
ELN exports, LIMS dumps, and CRO deliverables end up in separate schemas with no lineage. Every analysis starts with undocumented cleanup. The 'AI-ready data layer' never materialises.
1Trustworthy outputs
Confabulations · unverifiable claims · inconsistent results
LLM and agent outputs sound plausible and occasionally hallucinate. Without structured evaluation against held-out ground truth, you cannot tell which is which. Until it matters.
2Operationalization
POC-to-production gap · monitoring · change control
The prototype worked three months ago on a clean extract. Nobody ran the retrain after the CRO switched assay protocols. There's no alert when performance degrades. That's the gap.
3Governance & security (EU)
Traceability · human oversight · attack surface
EU AI Act timelines are real. Prompt injection and data poisoning aren't theoretical. Traceability and human override paths need to be designed in, not retrofitted after a regulator asks.
4People & throughput
Unclear ownership · tool sprawl · slow handoffs
Three teams own three parts of the pipeline. The data engineer doesn't know what the model needs. The ML engineer doesn't know what the clinician will trust. Nobody owns the decision end-to-end.
5If this sounds familiar, a 30-minute triage call costs nothing and clarifies a lot.
Book R&D Triage (30 min, free)The loop that makes systems reliable, not just accurate
How it gets fixed
Uncertainty
Reduction Loop
Every engagement starts with one question: what decision are you trying to improve, and how much uncertainty can you afford? Then the system gets built to answer it. Reliably.
Define the decision
Before building anything, the work starts with precision: what decision does the system need to support, and what does "good enough" look like in quantitative terms.
Test hypotheses (fast)
Design experiments to directly falsify the assumptions your system depends on. Evaluation-first, not build-first. Kill bad ideas quickly and cheaply.
Build the system
Once hypotheses hold, build the production system: pipelines, deployment, monitoring, and retraining loop. Decision-grade from day one.
What "custom AI systems" means
Strategy · Engineering · Modelling
Not a model. Not a dashboard. A complete system, from roadmap through production. The bottleneck determines the emphasis.
Strategy
Value-driven discovery
Turning ambiguous AI ambitions into a scoped, prioritised roadmap
What you get
- Decision interface definition
- Hypothesis prioritisation
- Build vs buy vs partner
- Stakeholder alignment
The most expensive mistake is building the wrong thing. Fast.
Learn more →Engineering
Throughput + reliability
Moving models from notebooks to reproducible production systems
What you get
- Training & serving pipelines
- Evaluation frameworks
- MLOps & monitoring
- Data platform & feature store
Reproducible is not the same as accurate. Both matter for regulated contexts.
Learn more →Modelling
Bespoke mechanism modelling
Domain-appropriate statistical, causal, and ML models for your decision
What you get
- Causal & statistical modelling
- Optimization systems
- Real-world evidence
- Domain-adapted NLP
Generic models rarely fit the causal structure of biopharma decisions.
Learn more →Book R&D Triage (30 min, free)
No agenda needed. Map the bottleneck and leave with a clear direction.
Uncertainty
Reduction Sprint
A focused 2-week engagement. We identify the key uncertainties blocking your R&D decision, design the experiments to resolve them, and hand you a ranked action plan.
You leave with
- 1
Plan
Ranked hypotheses + what to test next, with rationale
- 2
Pilot roadmap
Milestones, resourcing, and decision gates
- 3
Evaluation plan
What 'working' means, in measurable terms
- 4
Architecture sketch
Minimal reliable system, no overengineering
- 5
Risks & unknowns
Explicit, with mitigation strategies
Who it's for
- Teams at a pre-build inflection point
- Post-failure retrospectives needing a new direction
- Pre-scale validation before committing engineering resources
- Regulated teams needing a defensible AI plan
Who it's not for
- Teams with no data at all
- Projects where the decision is already made
- Purely exploratory research without a decision link
Sprint timeline
- Data audit
- Hypothesis mapping
- Evaluation design
- Stakeholder interviews
- Architecture sketch
- Risk assessment
- Pilot roadmap
- Decision memo
Format: remote · No full team required day 1 · Output: structured written deliverables
Proof it works
Systems that shipped
The blocker
Rule-based bidding heuristics had hit a ceiling. A/B test cycles took quarters. The team couldn't tell whether model improvements translated to real revenue uplift.
What was built
Closed-loop optimization system with a defined decision interface, experiment-grade evaluation pipeline, and hypothesis-to-evaluation harness.
Outcome
Validated uplift across held-out account cohorts. Hypothesis-to-evaluation cycle shrunk from quarters to days.
The blocker
Geriatricians document in unstructured Dutch free text. Structured extraction was needed, but every output had to be traceable back to source to get clinician trust.
What was built
Domain-adapted NLP pipeline for structured information extraction from Dutch geriatric consultation notes, with explainability surfacing source sentences.
Outcome
Pilot milestone reached: clinicians could follow model reasoning to source text. Annotation agreement metrics met clinical validation threshold.
The blocker
A formulary review required evidence beyond the RCT. Registry data existed but the causal question was confounded. Standard methods gave wide, unactionable intervals.
What was built
Causal modelling pipeline with propensity scoring, sensitivity analysis, and structured uncertainty quantification bridging RCT and real-world outcomes.
Outcome
Hypothesis confirmed at decision-relevant precision. Results integrated into a formulary review submission.
Have a similar challenge? A 30-minute call is enough to pressure-test your next step.
Book R&D TriageWhy me vs alternatives
One owner for the full system
Your PhD used Claude. Your team can prompt. Here’s what’s different when reliability, ownership, and EU AI Act readiness matter.
Option
Reliability
Eval harnesses, monitoring, change control
Speed
Iteration velocity + decision clarity
Ownership
End-to-end accountability, not fragments
EU AI Act readiness
Traceability, oversight, audit-ready artifacts
Internal team + coding agents
In-house engineers with LLM tooling
Big consultancy
Tier-1 strategy or IT firm
Freelancer / ad hoc
Individual contractor, task-scoped
Biolytics AI
Senior hands-on, full loop. One owner, no handoff gaps.
Who this is for
Find your context
The problems are different. The approach, decision-driven and evaluation-first, is the same.
Biotech & Pharma
R&D teams running discovery, trials, or evidence generation who need AI systems that are principled enough to act on. Not just impressive demos.
- Mechanism modelling & optimization
- Real-world evidence from registry data
- Hypothesis evaluation frameworks
- R&D data platforms
Healthcare Software
Product and engineering teams building AI features for clinical workflows, where adoption, explainability, and trust are the real barriers.
- Clinical NLP & note understanding
- AI pilot design & validation
- Workflow integration & trust layer
- Payer-aligned outcome modelling
R&D Triage
What happens in
30 minutes
No demo, no deck, no pitch. Just a structured conversation about your specific situation, and a framework for moving forward.
You describe what's stuck
Where's the project? What's the decision you're trying to support? What's the data situation? No prep required. Just plain language.
A framework to clarify the path
What type of problem is this (really)? Where does the uncertainty live? What gets tested first? What's likely to waste time?
You leave with a clear next step
Either a path forward you can execute internally, or a scoped proposal if you want to work together. No ambiguity.
You leave with
- 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 agenda, no pitch. Useful regardless
Book your R&D Triage
Takes 2 minutes to fill in. You'll hear back within one business day.
Operate and defend
EU-ready from day one
Traceability, oversight, and documentation are designed in, not bolted on after a regulator asks. Every system built here is defensible by default.
System compliance stack
Each layer is considered at design time, not patched in after delivery.
Curious how this applies to your system? Let's look at it together.
Book R&D Triage