Not the endpoint yet — the bigger knowledge lever first. The Advisor can say "I need 5 answers" but
does not yet decide what it can find out by ITSELF. The Silent Knowledge Pass runs in front of the
Advisor and, from signals existing scanners/parsers already produce (website, repository, documents,
product data), deterministically derives capabilities the company demonstrably HAS + product facts
that drive scope — so every recognised item shrinks the delta and removes a question.
compliance/onboarding/silent_intake.py: silent_intake(signals, signal_map) -> detected_capabilities
(+ evidence already in hand) + product_facts. The signal->conclusion map is curated DATA
(knowledge/onboarding/intake_signal_map.yaml), signals are injected (scanners are upstream). Pure,
deterministic, no LLM. advisor_start gains detected_capabilities (folded into the profile at HIGH
confidence -> covered, not asked) and an auto_detected result + headline.
The experience flips from a question wall to "we already recognised 4 capabilities, 2 product facts
and have 4 pieces of evidence in hand — only these few remain". Order now: Silent Pass -> #58
endpoint/frontend -> #59 empirical loop. NOT new architecture, just an orchestration step in front.
Non-runtime (no app caller) -> no deploy. 15 onboarding tests pass, mypy --strict clean, check-loc 0.
The user-named "right next runtime step": stop building knowledge, start using it automatically in
onboarding — no sales training, no regulation picking. compliance/onboarding/ is an ORCHESTRATOR (not
a new engine) wiring Company 2A -> RS-005 -> optimization -> completeness:
advisor_start(input, cert_hypotheses, target_requirements, ...) -> AdvisorResult
From (company + products + certifications + target) it returns inferred_assumptions, rejected_
assumptions, next_best_questions (<=5, ranked by information_gain + leverage + unknown_high_risk +
evidence_missing, each self-explaining), capability_delta, top_measures, evidence_requests,
unsupported_domains, completeness_summary. apply_answer() updates the profile (delta shrinks).
Welt-1 throughout: certificates REDUCE questions but satisfy nothing automatically (verification_
required); relevance(evidence,target) keeps ISO 14001 out of the CRA result. Certificate->capability
hypotheses + target requirements are INJECTED (curated knowledge, outsourced; not in code).
All 7 acceptance criteria pass; mypy --strict clean. First app-caller wiring the engines into a
product flow — still no endpoint/persistence, so 0 runtime effect -> no deploy yet (deploys when
POST /onboarding/advisor-start + frontend are wired). check-loc 0.
Benjamin Admin