The learning point is not the hypothesis, it is the QUESTION — and confirmed/refuted is too coarse.
"partial, only critical suppliers" or "certified but not lived" are not "wrong", they are valuable
knowledge. So the chain is Hypothesis -> Question -> Observation -> (Review) -> Hypothesis, and the
observation model must be defined cleanly before any store/API (else thousands of too-coarse
observations get migrated later).
compliance/onboarding/observations.py:
- ObservationType: confirmed / partial / refuted / not_applicable / unknown (richer than binary).
- Observation: {hypothesis_id, capability, question, answer (free text), observation_type,
scope_note ("only critical suppliers"), evidence_uploaded, reviewed, reviewed_by}.
- empirical_distribution() -> a DISTRIBUTION (confirmed 61 / partial 31 / refuted 8), not one %.
- empirical_confidence() -> (confirmed + 0.5*partial) / (confirmed+partial+refuted); n.a./unknown
excluded; None until calibrated.
- REVIEW GATE: only reviewed observations calibrate — a raw answer never changes a hypothesis (no
learning from outliers).
Refactor: the hypothesis is now PURE curated knowledge — the binary observations counter and any
confidence are removed from CapabilityHypothesis and the YAML; confidence is COMPUTED from the separate
reviewed observation stream. Pure, mypy --strict clean. Persistence/aggregation/calibration are 59b/c/d.
Non-runtime -> no deploy. 12 tests pass, check-loc 0.
The bottleneck is knowledge, not the endpoint. This builds the knowledge the Onboarding Advisor needs,
restructured per the user's key insight: NOT "ISO27001 -> 30 capabilities" but each hypothesis as its
own object "capability -> supported_by: [certs]". A capability is written ONCE with all supporting
certs, so the shared management-system core (document control, incident, supplier, audit, access,
asset, monitoring, training, crypto, release, risk) covers most certifications with ~18 hypotheses
instead of ~300 — and multi-certification merges AUTOMATICALLY (a company's inferred caps = every
hypothesis whose supported_by intersects its certs).
Welt-1 throughout: "IF cert present, EXPECT capability (verification required)", never "erfüllt".
Capabilities NO cert suggests (SBOM, signed updates, CVD, support period) have no hypothesis -> they
stay in the delta and get asked. confidence is EMPIRICAL: computed from real-onboarding observations
(confirmed/(confirmed+refuted)), None until calibrated — never an LLM/expert score (record_observation
+ empirical_confidence). The long-term moat: knowledge that learns from reality, not from a norm.
compliance/onboarding/hypotheses.py (resolve_for_certifications / inferred_hypotheses / empirical_
confidence / record_observation) feeds the existing advisor_start unchanged; the demo now runs on the
curated library. Pure, mypy --strict clean, library is DATA (no norm text, no real names). Non-runtime
-> no deploy. 12 tests pass, check-loc 0.
Benjamin Admin