Proposed SmartRecall proprietary framework
Adaptive Truth Alignment System (ATA) is a proposed six-module operational and observational working framework. It operates on the Enterprise Fact Layer, with human confirmation at every material decision.
Not a deployed platform, industry standard, model-alignment technology or real-time monitoring system.
Six modules form a loop with human gates
Adaptive Truth Alignment System (ATA) is a proposed six-module operational and observational working framework. It operates on the Enterprise Fact Layer, with human confirmation at every material decision.
Adaptive Truth Alignment System (ATA) is a proposed six-module operational and observational working framework. It operates on the Enterprise Fact Layer, with human confirmation at every material decision.
Inputs include confirmed facts, a source inventory, evidence, selected test scenarios, observable public outputs and owners.
Outputs include variance records, an Evidence Cadence Ledger, approved corrective actions and the next review point. ATA does not infer model internals.
Activation, tracking and adjustment form a repeatable cycle with evidence retained at each pass.
Input: confirmed facts, definitions, language variants, prohibited forms and owners. Work: normalise, version and approve. Output: baseline entries.
Source-supported State, date, owner, source and change history.
Applicable scope Accuracy depends on facts supplied and confirmed by the client.
Input: baseline and named-entry snapshots. Work: compare fields, dates, versions, omissions and conflicts. Output: variance map and priority queue.
Source-supported Source reference, observed value, baseline value, date and disposition.
Applicable scope Not a complete internet scan; covers agreed, accessible entries only.
Input: source ownership, evidence relationship, update control and relevance. Work: classify under internal criteria. Output: source class and handling rationale.
Source-supported Criteria, named source record and reviewer decision.
Applicable scope Not a search-ranking weight, platform score, certification or endorsement.
Input: baseline meaning, source text, language and context. Work: compare scope, audience, time, qualifiers and omissions. Output: variance for human review.
Source-supported Side-by-side text, rationale, severity and reviewer decision.
Applicable scope Cannot automatically prove error, falsity or intent.
Input: approved questions, region, language, visible system version, time and baseline. Work: capture and compare public output. Output: a dated sample.
Source-supported Scenario, time, system label, output reference and limitation note.
Applicable scope Samples vary, are non-exhaustive and provide no access to model internals.
Input: confirmed variance, approved correction, access and entry scope. Work: prepare, approve, submit, record and schedule a recheck. Output: deployment and exception records.
Source-supported Change or submission record, visible state and next observation date.
Applicable scope Platform state: acceptance, indexing, ranking, citation and answers are observed separately.
An accountable person decides the current fact, qualifiers and prohibited variants.
A reviewer decides whether a difference is material or needs more evidence or a business decision.
A change proceeds only after its access, wording, entry and risk are approved.
All labels below are synthetic and demonstrate record structure only.
Evidence Cadence Ledger example. All labels below are synthetic and demonstrate record structure only.. Observation: A named entry omits one approved condition from the service audience. Entry reference, observation date, baseline version and capture state. Decision: The owner confirms that the condition remains applicable and requires correction. Decision date, confirming role and wording version. Action: An entry update is prepared and awaits access approval. Action owner, state, exception and next review point
This is a work-and-evidence record, not real-time telemetry or a performance dashboard.
The sample header records the report date, baseline version, named entries, observation question, language and any identifiable system or version.
A one-page action report separates completed work, visible results and open items. The sample header records the report date, baseline version, named entries, observation question, language and any identifiable system or version.. Completed work: List only the inventory, confirmation, structuring, update, submission or recheck work completed and evidenced during the period. Action ID, owner, completion date and deployment or submission record. Observable result: Record what was visible at a named entry or public scenario on a stated date, then compare it with the confirmed baseline. Entry, question, language, date, conditions and visible variance The observation represents that scenario only. It does not infer model internals or stable performance.. Unresolved gap: Identify anything still stale, conflicting, inaccessible or awaiting a decision, with its operational effect. Gap, cause, materiality, owner and current state. Uncertainty and third-party state: Keep unknown versions, platform approval, indexing delay, refusal reasons and uncontrollable factors in a separate record. Known conditions, unknowns, third-party response and last-check date A completed submission or check does not mean third-party adoption.. Next action and trigger: State the next executable action, its owner, required input, and when a business change or review cadence should reopen the item. Action owner, dependency, target review point and closure condition
This is an anonymous synthetic template, not a client report, live telemetry, performance data or proof of outcomes.
Record the test question, the fact under review and why observation is needed.
State the language, market and local context that may affect presentation.
Record the identifiable platform, product or version; mark it unknown when it cannot be confirmed.
Retain time, sign-in state and known conditions so a sample is not mistaken for a stable output.
Preserve the visible answer, citation or entry and the specific difference from baseline.
State what the sample cannot represent, who must decide and when to retest.
AUC can structure five review dimensions at baseline and later review. Any readout must travel with its date, data scope, questions, system versions and method limits.
Whether names, contacts, address, operating scope and core identity are clear, accurate and consistent.
Source-supported Confirmed baseline and named entries
Whether services, products, intended audiences, conditions and differentiation support reasonable understanding.
Source-supported Service definition, qualifiers and observation sample
Whether material facts remain consistent across websites, directories, social channels and agreed entries.
Source-supported Variance register, date and state
Whether material statements connect to verifiable sources, third-party evidence and appropriate governance.
Source-supported Evidence register and source classification
Whether named material entries are in scope and can be updated following significant change.
Source-supported Entry list, update record and remaining gaps
AUC does not claim recommendation, ranking, mention frequency, universal platform coverage or final answers.
ATA is
It registers a baseline, compares named sources, records samples, retains human decisions and schedules controllable correction.
ATA is not
It does not read model parameters, training data, private indexes or ranking logic, or force an answer to change.
ATA's role and limits. A scoped working framework: It registers a baseline, compares named sources, records samples, retains human decisions and schedules controllable correction.. AI control or model-access technology: It does not read model parameters, training data, private indexes or ranking logic, or force an answer to change.
The Enterprise Fact Layer provides the confirmed comparison point. ATA organises variance, observation, decisions and corrective cycles. The methodology assigns an owner, evidence and limit to each step.
Together they provide governance discipline without rewriting external correlation as a controllable causal result.
See how SmartRecall turns the baseline and ATA loop into phased work with owners, evidence and limits.