Answer Assurance activates whenever a result makes factual claims, cites evidence or must demonstrate that the requested answer contract has been covered before publication.
Answer Assurance
Prefer a safe partial answer to a polished unsupported one.
Builds a canonical evidence ledger, validates scoped references, applies per-source lexical and structural claim checks, measures requested-facet coverage and exposes precise repair gaps.
A publication decision in which bracketed citations resolve to canonical admissible references, each factual unit passes a bounded same-source lexical and structural check, coverage is measured, and unresolved gaps remain visible.
Why it exists
The problem this module is designed to solve.
Generative systems can attach a citation that does not support the sentence, merge evidence from incompatible scopes or cover only the easiest part of a question. Fluency hides these failures unless publication is controlled by an evidence-aware boundary.
02 / How it works
A bounded path from need to accountable result.
The public model below describes responsibilities and decisions, not sensitive implementation details, provider secrets or customer data.
- 01
Assemble the evidence ledger
Normalize candidate support into one scoped record of sources, passages, claims and provenance relationships.
- 02
Authorize claims and citations
Require each factual unit to be lexically covered by one admissible detailed evidence item and each declared bracketed citation to resolve to a canonical reference.
- 03
Measure contract coverage
Compare the draft with the requested facets and identify exactly which obligations remain unresolved.
- 04
Publish, repair or abstain
Allow supported material, request bounded repair for a precise gap or return a useful partial answer with visible uncertainty.
03 / Customer and operator value
Higher citation fidelity, fewer orphan assertions and more useful abstention when available evidence cannot support the answer.
Canonical claim-to-evidence ledger
Citation authorization
Deterministic exact-evidence publication without a model call
Safe partial publication
Gap-targeted repair contract
04 / Where it creates value
Concrete situations, not generic feature claims.
These are representative product situations. Every deployment still requires its own policy, data boundary and acceptance criteria.
Policy and compliance answers
Prevent confident publication when the available controlled documents do not support the requested conclusion.
Research synthesis
Keep source attribution attached to individual claims across several documents and external evidence items.
Decision-support briefs
Show leaders what is supported, what is missing and where more evidence is required before action.
05 / Role in the cognitive system
A clear responsibility creates a trustworthy boundary.
No module is allowed to become an invisible monolith. It owns a narrow contract, composes with named capabilities and refuses responsibilities that belong elsewhere.
What it owns
- Claim authorization
- Citation fidelity
- Facet coverage
- Publication safety
What it composes with
Question ContractDocument RetrievalExternal ResearchCognitive Engine
What it refuses to own
Boundary before convenience.
It does not retrieve evidence, run inference, write memory or prove semantic entailment. Meaning-sensitive publication still requires the upstream semantic verifier or human review.
06 / Vision and mission
Evidence into accountable action
This module is where Mentaview's purpose becomes a publication rule: evidence must remain connected to the claims it supports, and uncertainty must stay visible when action is not yet justified.
AI systems that remain useful, inspectable and sovereign across models, providers and deployment boundaries.
Build the cognitive layer that chooses the smallest sufficient path and turns evidence into accountable action.
Capture the value of advanced AI without surrendering data control, architectural freedom or intellectual honesty.
07 / Evidence and maturity
What the current label means — and what remains open.
ENGINEERING MATURITYM4Mentaview internal laboratory assessment
OBJECTIVE REVIEW
Passed with moderate confidence
Claim, citation and publication controls execute in the integrated answer path and pass local adversarial checks; semantic entailment and independent human review remain outside the proven boundary.
OPEN LIMITS
- The 40-case blind human review remains pending.
- Lexical and structural assurance does not prove semantic entailment.
- No customer-DMS or independent operating evidence.
What exists today
The independent ledger, citation validation and publication guard remain implemented. The sealed Wave 11 machine holdout passed 40/40 across five languages with zero orphan claims, zero critical false-completes and no extra model call; independent review of the generated blind packet is still open. A separate bounded metric-plus-receipt path enforces exact same-source evidence and rank citations with zero provider calls. This publication evidence belongs to Answer Assurance, not Memory, and remains lexical and structural rather than semantic entailment.
Implemented
A software or contract boundary exists in the current development project. This does not by itself mean general availability or independent certification.
What must earn promotion
Complete the generated 40-case blind human review and extend the frozen holdout across customer domains, ambiguous sources, token-preserving meaning changes and deliberately conflicting evidence.
Public truth boundary: M4− is a non-standard Mentaview engineering label for internal laboratory validation. It is not an official TRL decision and does not assert production qualification, customer acceptance, independent assurance, certification or universal performance. Inspect the complete assessment record.