BMAIAI that classifies evidence — not one that guesses
BMAI's AI layer turns raw Meta signals into structured, explainable findings. It does not generate plausible-sounding explanations when Meta hasn't provided one.
Structured classification
Findings are mapped against Meta's own documented codes and fields — restriction types, severities, and official remedy paths — not inferred from patterns.
Case analysis
Related signals are grouped into a case with a root-cause assessment, so recurring issues are visible as one thread rather than repeated alerts.
Confidence-aware recommendations
Recommended actions carry a confidence score. Low-confidence cases are routed for human review rather than acted on automatically.
Four-way evidence taxonomy
Every signal BMAI reports is one of: verified by Meta, blocked by a Meta permission limit, not exposed by Meta's API, or unknown. A missing explanation is never silently upgraded into a conclusion.
Learning from outcomes
Resolved cases feed back into future recommendations for similar signals, scoped per connection so one customer's history never leaks into another's.
Human approval, always for writes
AI proposes; a person disposes. Any action that reaches Meta requires an explicit approval step, and autonomous submission is a setting you control.