1
Brief safely
Start with sanitized context and a concrete reliability decision.
Flagship service
Evaluation artifacts for deciding whether an AI workflow should ship.
The sprint is for teams with an existing agent, RAG workflow, model pipeline, paper, repository, trace set, or dataset. The work turns uncertain quality debates into reproducible evidence and a decision record.
Sanitized brief first
No private repo in form
Artifacts delivered
Ship / revise / stop memo
2
Reproduce failures
Turn traces, examples, or eval runs into repeatable evidence.
3
Define gates
Separate blockers, warnings, thresholds, and ownership.
4
Decide ship / revise / stop
Package the evidence into an engineering-readable memo.
Good fit
- The team has a working AI workflow and a release or continuation decision to make.
- Failures appear in traces, tickets, reviews, or eval runs but are not reproducible enough for engineering action.
- The buyer wants reusable evaluation artifacts rather than a one-off opinion.
Boundaries
- No credentials, secrets, raw customer records, production data, or private repository access in the public brief.
- No unsupported security, compliance, deletion, or guaranteed outcome claims.
- Generic chatbot builds without a reliability decision are not the intended fit.
Deliverables
- Evaluation plan and replay/test surface
- Failure taxonomy with reproduction notes
- Release-gate checklist and threshold rationale
- Ship, revise, or stop decision memo
- Handoff notes for the client engineering team
Inspect the artifact shape
The sample is representative structure, not client data. It shows how evaluation plans, taxonomies, gates, and decision memos can be packaged without inventing proof.
View artifact sampleProcess
How the work moves from brief to evidence.
01
Start with a sanitized technical brief and a scoped reliability decision.
02
Agree the workflow boundary, available inputs, access rules, and artifact list.
03
Reproduce representative failures and organize them by trigger, severity, and likely owner.
04
Define release gates and summarize the evidence in an engineering-readable decision memo.
Next step
Have a workflow ready to evaluate?
Send sanitized context first, then use the call to confirm fit, access boundaries, and scope.
Related technical notes
Further reading for this service.
How to Decide Whether an AI Workflow Ships, Changes, or Stops
A decision framework for AI teams that need evidence before shipping, revising, or stopping an agent, RAG, or model workflow.
Release Gates for LLM Features
How to define blockers, warnings, thresholds, and decision records before releasing LLM-backed features.