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Research · validation methodology

How ENRIVAQ Validates AI-Enriched Product Data

AI enrichment makes product research and structuring faster, but technical catalog data is not accepted because a model produced a fluent answer. Validation is the control layer between candidate data and the product record.

The purpose is to stop wrong product matches, unsupported values, inconsistent units and unresolved source conflicts from silently becoming publishable data.

ENRIVAQ validates candidate product data before it becomes usable catalog output. Public documentation explains the quality principles and observable decisions; proprietary scoring, routing and model logic remain internal.

Product data validation
What validation prevents BLOCKED BEFORE PUBLICATION
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A fluent answer is not evidence. Every accepted value carries a source and a check. 10 sections · 7 validation layers
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The layers describe the framework. The rule set as implemented in production is published separately. Identity and source context must match the target product. Accepted values require source provenance and a valid canonical mapping; ambiguous or conflicting evidence is held; invalid or wrong-product evidence is rejected.
Data quality control
Source selection

Validation starts before extraction

A technically correct value taken from the wrong product page is still wrong for the target record. Source checks confirm that the candidate source is relevant to the identified product, using the identifiers and product context actually available in the workflow.

Match the candidate source to the target product using available identifiers and product context. Preserve source evidence for accepted/reviewable values and reject wrong-product or irrelevant pages.

Source check · decision BEFORE EXTRACTION
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A source that fails the relevance check is not used, even when it contains the requested field.
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Ambiguous identity ends in UNRESOLVED or REVIEW — never in inherited specifications.
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Reference example · AC820825: OEM / MPN, EAN and Optima / Optima HD compatibility are supported; the conflicting weight values are held for review.
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Conflict state · what the system does NO SILENT SELECTION
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Candidates and their sources stay attached to the field so a reviewer can decide with evidence.
Confidence

A prioritization signal, not proof of correctness

Confidence helps decide what a reviewer looks at first. The methodology defines what the score represents, how it is derived where disclosure is possible, and how it is used operationally.

ENRIVAQ uses task-specific confidence signals as operational inputs alongside provenance, identity, mapping and conflict checks. Exact internal scoring logic is not presented as a probability of correctness.

Question the methodology must answer OPERATIONAL VALIDATION MODEL
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A high score never replaces a source. Values still carry their evidence.
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What the reviewer sees on the value CURRENT EVIDENCE / ROUTING VIEW
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A reviewer decides on context and evidence, never on an unexplained AI output.
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Criteria differ per catalog and category, so they are published per catalog rather than as one global rule. Acceptance is defined per project/category: required fields, provenance coverage, valid canonical mapping and no unresolved hard conflict in an accepted field.
Product data quality
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An unresolved field is a valid outcome. A plausible value without evidence is not.
Keep reading

Validation, traceability and provenance pages

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