Uncertainty Reduction
Sprint
A focused 2-week engagement. The sprint surfaces the key uncertainties blocking your R&D decision, designs the experiments to resolve them, and delivers a ranked action plan.
6
artifacts delivered
3 gates
data, model, scope
day 10
final handoff
You leave with
6 artifacts. Written. Yours to keep.
Regardless of what happens next. These are not slides; they are working documents.
- 01
Decision interface spec
The decision, owner, criteria. Written before any data work begins.
- 02
Data audit + gap analysis
What exists, what is missing, what the hypothesis actually needs.
- 03
Evaluation harness v1
The scaffolding to run experiments consistently and repeat them.
- 04
Ranked hypotheses
Ordered by impact × effort. Each with confidence level and rationale.
- 05
Architecture sketch
Minimal reliable system. No over-engineering. No black-box choices.
- 06
Pilot roadmap
6–10 week plan with milestones, resourcing, and decision gates.
Convinced? Let's check fit first.
The triage call is 30 min. You leave with a clear answer on whether this makes sense for your situation.
When the sprint is a fit.
The triage conversation is the real filter. But this should give you a rough sense before the call.
Good fit
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You have a decision to make this quarter — what to build, what to trust, whether to proceed.
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You have data access, even if messy, partial, or undocumented.
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Your team is blocked by uncertainty, not by lack of effort or budget.
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You need alignment across research, engineering, or product stakeholders.
Not a fit
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No decision owner. No one accountable for acting on the outputs.
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No access to any data, constraints, or domain knowledge.
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Purely exploratory research with no path to workflow or action.
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You want guarantees rather than a validated plan and ranked options.
Fit matrix
High Uncertainty The Unknown How
Low Uncertainty The Known How
Must-Win
✅ The Sweet Spot
Ideal fit
A critical business directive exists. Data is accessible. The technical path is blocked by unknowns you need mapped before committing an engineering team.
⚠️ The Execution Challenge
You may not need me
The path is clear and the technology is proven. This is a resourcing problem, not an uncertainty problem. You need engineers to build, not R&D to explore.
Exploratory
❌ The Science Project
Don't do this
Pure exploratory research without a tied business outcome or a decision-maker waiting for results. I only engage when a concrete decision is waiting to be made.
❌ The Commodity Feature
Don't do this
Low-impact features using off-the-shelf APIs with already-known outcomes. Spending R&D budget validating things that already exist erodes your edge.
What sprint outputs look like.
Mocked for confidentiality, but structurally accurate. These are representative of real artifact formats.
One-page plan (PDF-style)
Decision spec, top hypotheses, eval approach, next steps. One page.
Hypothesis map
Every R&D bet ranked by testability and expected impact on your decision. High-confidence hypotheses connect to the decision node; low-signal ones are visibly parked.
Why it matters
- Stops teams from chasing 10 hypotheses at once with no sequencing
- Signal/noise scores make priority arguments objective, not political
- Pruned after sprint tests so the map stays current, not stale
Pilot roadmap (milestone view)
6-week milestones, owners, risk gate markers.
FAQ
Questions worth asking
before committing.
Objections, clarifications, and what to expect.
Sprint artifacts are designed with EU traceability and oversight expectations in mind. EU AI Act ↗
GenAI used as hypothesis generator, not source of truth. Prompt injection threat models included. ISPE ↗
Confabulation acknowledged per NIST AI RMF for GenAI. NIST ↗
Book AI R&D Triage
Book the 30-min triage,
you leave with a plan.
No pitch, no deck. A structured conversation about your specific blocker.
Before the call: helps me prepare