3
live fact conflicts found in the first automated drift report
First drift report (run before the pilot began): two main sites named two different companies; the headline question count read 4,000+ on the live site while the team's official figure was 5,589 — a number appearing on neither site. The third conflict was a missing brand asset discovered during the same scan.
The problem
How it worked before
What we built
What changed
System shape
Inputs
Six products' public surfaces + internal fact documents
Processing
Canonical Product Brain + automated drift detection
Outputs
One authoritative reference + drift reports flagging contradictions
Method
- 1Map: audit all public surfaces to surface every fact conflict and the root cause
- 2Structure: design a canonical Product Brain schema covering figures, pricing, positioning, and brand identity
- 3Implement: build the queryable reference and automated drift-detection pipeline
- 4Adapt: refine the schema against real drift reports and team feedback
Constraints
- Six products with distinct brand identities
- Public surfaces only — no internal access required for the pilot
- Draft-first approach: nothing changes without CEO sign-off
Our role
Why this is anonymized
Client identity is withheld under engagement confidentiality. The metrics are real and verifiable in a private reference call.
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