Uncertainty reduction is the product.
A repeatable loop turns ambiguous R&D questions into validated hypotheses and decision-grade systems, designed to hold up under real constraints.
6-step
uncertainty reduction loop
4 gates
before anything ships
EU-aware
delivery defaults
Decision-grade: a demo is not enough.
Every system is evaluated against this checklist. It’s the difference between something that works in a notebook and something a regulated organisation can rely on.
Inspection checklist
10 criteria that separate a demo from a decision-grade system.
EU AI Act high-risk expectations emphasize traceability, human oversight, robustness and cybersecurity. digital-strategy.ec.europa.eu ↗
Validation gates: where most “agentic” projects fail.
Four mandatory gates, each with pass/fail criteria. Nothing progresses to the next stage without clearing the previous one.
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Schema consistency + version checks
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Distribution integrity (no leakage)
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Missing / corrupted value thresholds
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Source provenance documented
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Train/val/test splits locked
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Baseline comparison (naive + prior art)
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Uncertainty quantification present
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Calibration check (not just accuracy)
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Subgroup / slice evaluation
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Edge-case coverage in eval harness
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Confabulation / hallucination tests
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Prompt injection threat model
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Output provenance traceable
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Grounding / retrieval quality checks
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Failure mode catalog documented
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Monitoring + drift detection plan
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Change control procedure defined
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Rollback / fallback path tested
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Audit logging in place
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Incident response sketched
How work moves from ambiguity to shipped system.
Five stages, each producing tangible artifacts. Timelines are estimates, what matters is the decision spec comes before the model.
See what stage 1–3 looks like in two weeks.
The Uncertainty Reduction Sprint is stages 1–3 as a fixed-scope engagement.
How to work together.
Two engagement modes. One tests a hypothesis in two weeks. The other builds the system over a few months.
Uncertainty Reduction Sprint
A structured 2-week engagement delivering a tested hypothesis and an actionable plan. You go from unclear question to credible answer with evidence.
Deliverables
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Decision spec (written)
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Data reality map + gap analysis
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Tested hypothesis + eval harness
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Next-step recommendation with cost/risk
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Session recording + written summary
Outcome: plan with tested hypotheses
Learn moreEmbedded R&D Systems
2–4 days/week for a few months. Hands-on senior builder across the full loop. Not oversight, not advice. Compounding frameworks and team enablement included.
What this covers
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Full loop: decision to model to system to operations
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Evaluation harness design + ownership
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System architecture + implementation
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EU-aware delivery patterns (traceability, oversight)
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Compounding frameworks your team can build on
Starts with a triage to assess fit
Book triageWhat it takes.
Clear expectations on both sides. This is also a pre-qualification filter, misalignment here is better surfaced in the triage than three weeks in.
You bring
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Decision owner
A specific person who can approve the direction and act on the output.
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Access to relevant data
Not necessarily clean, but accessible, documentable, and relatable to the decision.
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Unblocking infra/security
Someone on your side who can answer compute, access, and security questions without 6-week queues.
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Willingness to test + measure
An appetite for evaluation-first development. If the goal is moving fast without measuring, we're not well-aligned.
What's brought
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Method + artifacts
Decision spec, hypothesis map, eval harness, pipeline diagrams. Not slide decks.
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Evaluation discipline
Every component has a clear definition of "working" before any build begins.
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System design + implementation
Built hands-on. Senior-level engineering across data, models, infrastructure, and deployment.
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EU-aware delivery mindset
Traceability, audit logging, and human oversight are defaults, not add-ons. No legal compliance guarantee.
Not a fit if…
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No identifiable decision owner or user
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Purely exploratory with no path to actual usage
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Cannot access or describe any relevant data
Triage conversations are short precisely so we can both figure this out early.
EU AI Act & NL context
Traceability, oversight, robustness, and cybersecurity are built-in defaults. NL phased enforcement and NEN standards-in-progress noted. business.gov.nl ↗
Security-aware GenAI
Prompt injection and data poisoning threat models included for any system using LLMs. ispe.org ↗
GenAI risk framework
Confabulation and system-level risks acknowledged per NIST AI RMF for GenAI. nist.gov ↗
Book triage
Book the 30-min triage , you leave with a plan.
No demo, no deck, no pitch. A structured conversation about your specific situation: what's blocked, what's possible, what makes sense next.