BHASM reads observable customer behaviour — purchase history, engagement signals, cycle position, world context — and produces three things per customer per cycle: a score, a recommended action, and the reasoning behind both.
It does not invent intent. It does not synthesize facts. Every score is derived from data the operator's own systems already record.
For any customer the operator can ask "why this score?" — and BHASM returns a list of contributing factors with relative significance:
Silence duration — 45 days since last order · primary signal
Relationship tenure — 20 orders to date · moderate
Engagement state — active · moderate
Purchase consistency — steady cadence · minor
Every decision the engine takes is recorded with the active world signals at decision time and the safeguard pathway that approved it. The operator can replay any decision.
Exact model coefficients, aggregation formulas, and proprietary calibration logic are protected intellectual property. This ensures the engine remains effective, continuously improving, and difficult to replicate. All decisions remain fully auditable for compliance and review needs.
Every outbound action passes through a safeguard stack before firing. The stack enforces:
BHASM tracks every intervention's outcome — whether the customer returned, churned, or stayed inconclusive — and reports aggregate precision, recall, and false-positive rates per evaluation window. The operator sees this on their dashboard. Investors and enterprise buyers can request the underlying methodology.