Brief safely
Sanitized context and one concrete reliability decision.
01Flagship 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.
Reproduce
Turn traces, examples, or eval runs into replayable evidence.
Define gates
Separate blockers, warnings, thresholds, and ownership.
Leave a record
Connect the evidence to a ship, revise, or stop call.
02Scope ledger
What enters the engagement—and what leaves it.
A strong fit
01The team has a working AI workflow and a release or continuation decision to make.
02Failures appear in traces, tickets, reviews, or eval runs but are not reproducible enough for engineering action.
03The buyer wants reusable evaluation artifacts rather than a one-off opinion.
What your team keeps
01Evaluation plan and replay/test surfaceReusable within the agreed project scope.
02Failure taxonomy with reproduction notesReusable within the agreed project scope.
03Release-gate checklist and threshold rationaleReusable within the agreed project scope.
04Ship, revise, or stop decision memoReusable within the agreed project scope.
05Handoff notes for the client engineering teamReusable within the agreed project scope.
03Working sequence
From uncertain behavior to an inspectable decision.
The sequence is deliberately legible: agree the boundary, reproduce what matters, then make the release rule explicit.
Start with a sanitized technical brief and a scoped reliability decision.
Agree the workflow boundary, available inputs, access rules, and artifact list.
Reproduce representative failures and organize them by trigger, severity, and likely owner.
Define release gates and summarize the evidence in an engineering-readable decision memo.
04Open method
Inspect the reasoning before you engage.
These notes expose the decision framework behind the work. They are methodology, not disguised client proof.
01Technical noteHow to Decide Whether an AI Workflow Ships, Changes, or StopsA decision framework for AI teams that need evidence before shipping, revising, or stopping an agent, RAG, or model workflow.02Technical noteRelease Gates for LLM FeaturesHow to define blockers, warnings, thresholds, and decision records before releasing LLM-backed features.
09Safe first step
Bring the decision your team needs to defend.
Describe the ai reliability sprint context in sanitized terms. We will use the first exchange to confirm fit, evidence available, and the safest next step.
No credentials, production data, customer records, or private repository access in the first brief.
Prefer to talk it through? Request a 30-minute call