Pilot-to-Production Review sample

What the review delivers, shown on a real system.

The four-part operating design a two-week review produces, filled in with StreamGist's recommendation system. A client version covers one of your workflows.

1. Operating design

Who decides what.

Code owns anything that could corrupt data, spend, or production. Models do bounded judgment and writing. People own the rules and the irreversible calls.

DecisionCodeModelPerson

Which games a streamer seesFilters and ranksFrom stored dataNot in rankingCache-first explanationsOwns the ranking rulesNo per-request approval

Game research and scoringSource allowlistSchema check, atomic writeResearches and scoresAgainst a supplied rubricSets rubric and sources

Recommendation policy changesShip and cooldown gatesBackup, exact previewProposes bounded changesFrom outcome evidenceMerges the decisionWeekly decision packet

Operational alertsOwns thresholdsAnd severity tiersWrites the briefingFrom computed factsDecides what to act on

Public articlesSource packetRelease gatesDrafts and fact-checksEvery factual unitOnly the founder publishes

2. Failure modes and fallbacks

What still works when something breaks.

Each failure has a planned fallback before launch, not after the incident.

Model provider down or wrong: Ranking never calls a model, so recommendations keep serving.
Research fails for one game: Atomic writes keep its last good state. Other games continue.
A model changes behavior: Requested and served models are logged. Replays come before any switch.
A wrong score spreads: Drift monitoring watches tags, rankings, and copy.
Feedback only sees what it showed: Cohort controls and fixed experiments separate quality from exposure.
A job stalls quietly: Every job is checked every ten minutes. Unknown is never reported as zero.

3. Success metrics and baseline

What gets watched.

Every metric has an owner and a baseline before anything changes.

Research cost per completed game
Score and tag distribution drift
Save rate by cohort vs. the 21.4% baseline
Job freshness, failed writes, rollbacks
Reviewer holds and founder edit burden

Line chart of one game's StreamGist score over 14 days, from September 11 to September 24, 2026, drifting down from about 113 to 104. — Drift watch in practice: one game's GistScore over two weeks, from a public StreamGist game page.

4. Go/no-go readout

StreamGist's own call.

A client readout adds a 90-day roadmap sequenced from findings like these.

Hold: freeze and observe

Authority boundaries and evidence lineage held up. The adaptive recommendation loop needs production evidence, not another redesign.

Reopen only on

A repeated production failure
A measured cost concentration
A frozen replay that shows a gap

Want this for one of your AI workflows?

Two weeks, one workflow. Write a few sentences about it and I will follow up.

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