Case study · creator measurement · verified replay
Every creator campaign claim has to earn its evidence.
Proof Lab reconciles platform aggregates, keeps attribution windows and baselines visible, fails closed on missing denominators, and stops at a measurement-owner approval gate.
Creator Campaign Proof Lab demonstrates how deterministic metrics and bounded agent responsibilities can produce a defensible claim register instead of turning incomplete platform data into a polished causal story.
Case-study figures describe this documented engagement and are not forecasts or guarantees.
The operating constraint
Creator reporting frequently mixes incompatible platform metrics and presents attributed conversions as causal impact. Missing baselines or denominators can disappear inside a confident narrative.
The system Aixcel designed
Aixcel built a typed council for data contracts, attribution, incrementality, integrity, and evidence narrative. Arithmetic is deterministic; unsupported claims remain unknown; a broken denominator disables report approval.
The documented result
The complete live scenario produces supported observed and baseline-backed claims. The broken scenario returns 67% coverage, marks ratio evidence unknown, and blocks approval. Python tests, Postman assertions, and responsive browser journeys pass.
System components
Python 3.12, FastAPI, Pydantic, deterministic metric evaluation, REST/OpenAPI, Postman, Docker, GitHub Actions, Vercel
How to interpret this evidence.
Names and sensitive details are withheld. Metrics retain their stated meaning and evidence label. A scope count is not converted into an outcome, and no engagement result is presented as a universal benchmark.
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