Validated ≠ Leaderboards

Benchmarks are useful—but they don’t prove your model is ready for real work. I design fit‑for‑purpose evaluations and the operational controls that make AI/LLMs both useful in R&D and auditable when stakes are higher. 

What I Bring

.Context of Use → risk: we define the decision, users, and failure modes so evidence matches impact.

  1. Traceable, domain data: eval sets with lineage (ALCOA+), leakage checks, and realistic edge cases.

  2. Pre‑registered acceptance criteria: metrics, thresholds, sample sizes—agreed up front.

  3. HITL built‑in: review thresholds, work instructions, training.

  4. Lifecycle ready: monitoring/drift KPIs, owners, alerts, golden‑set cadence.

  5. Change control for retraining: triggers, impact assessment, rollback, release notes.

Benchmark Theater

vs

Real Validation

Fit‑for‑Purpose = meets pre‑registered, risk‑aware criteria on traceable data for the decision it supports—and can be operated, monitored, and changed under control.

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R&D Fit-for-Purpose Sprint

Packages

Deliverables:

  • CoU & risk rubric

  • Eval set + error taxonomy

  • Acceptance criteria

  • Small pilot

  • Decision memo

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GxP Validate -Launch

Deliverables:

  • Validation protocol

  • Report

  • Supplier qualification

  • Change control

  • Monitoring/drift

  • Audit pack

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Monitor - Improve (retainer)

Deliverables:

  • Validation protocol & report

  • Supplier qualification

  • Change control

  • Monitoring/drift

  • Audit pack

Curious if you’re fit-for-purpose today?

Book a 20‑minute fit check. I’ll walk through the scorecard, flag gaps, and recommend the smallest experiment that proves value.