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Product Experience Management

AI enrichment that shows its sources

Missing attributes and descriptions are drafted from your supplier documents, and every proposed value points to the sentence it came from.

Walk through it All capabilities

How it works

Review proposals for a solenoid valve

Select a proposal to highlight its evidence. Grounded values can be accepted; the IP rating conflicts with its evidence and the certification has no source, so neither can be accepted.

SOURCE · SUPPLIER DATASHEET P.1

Model SV-40 solenoid valve. Body material: forged brass. Port size 1/2 in NPT. Operating pressure 0 to 150 psi. Coil voltage 24 VDC, IP65 enclosure. Normally closed. Suitable for water, light oil and inert gas.

Proposed attributes for SV-400 accepted

Ungrounded proposals cannot be accepted until a reviewer supplies evidence.

The capability

Evidence first, generation second

Technical buyers punish inaccurate specifications. Generic AI writing tools produce fluent copy but invent ratings, certifications and dimensions that were never in the source. For industrial products that is a liability, not a productivity gain.

Autonera's enrichment extracts attribute values from datasheets, manuals and supplier files and attaches the exact passage that supports each value. A grounding check confirms the value is actually present in the cited evidence. Proposals without evidence, or whose value differs from the evidence, are held for a reviewer.

Descriptions and feature bullets are drafted from accepted attributes rather than from open-ended prompts, then checked against channel length and style rules. Reviewers keep final authority, and every accepted value records its source and approver.

Problems it solves

Where this work usually breaks down

  1. 01

    Thousands of empty attributes

    Supplier files carry the data, but nobody has time to transcribe it.

  2. 02

    Hallucinated specifications

    Generated copy claims ratings or certifications the product does not have.

  3. 03

    Unreviewable output

    Reviewers cannot see where a value came from, so they re-check everything.

  4. 04

    Inconsistent descriptions

    Every writer structures descriptions differently across categories.

Functional capabilities

What AI Product Enrichment includes

Source-grounded extraction
Extract attribute values from datasheets, manuals and supplier files with cited passages.
Grounding checks
Verify that each value appears in its evidence before it can be accepted.
Attribute-driven copy
Draft descriptions and bullets from accepted attributes and category templates.
Reviewer authority
Accept, edit or reject proposals individually or in reviewed batches.
Style and length rules
Apply terminology and channel limits to generated content.
Provenance
Record the source document, passage, model and approver for every enriched value.

In industrial operations

A valve distributor fills technical gaps across 12,000 SKUs

Supplier PDFs contained pressure ratings, materials and voltages that were missing from the catalog, limiting faceted search.

  1. Datasheets are linked to products.
  2. Values are extracted with cited passages.
  3. Grounded proposals go to category reviewers in batches.
  4. Accepted attributes drive standardized descriptions.

Result: Search filters work across the assortment, and every value can be traced to a supplier document.

Autonomous operations

How autonomous execution changes ai product enrichment

Agents work through enrichment backlogs continuously, but only grounded, reviewed values reach the product record.

Agents carry out

  • Find products with missing attributes and available sources
  • Extract values with evidence
  • Draft descriptions from accepted data
  • Route ungrounded values for review

People decide

  • Approve values for regulated attributes
  • Edit descriptions
  • Decide when evidence is sufficient

Evidence recorded

  • Source passage per value
  • Grounding result
  • Reviewer decision

Related capabilities

Works alongside

Feature guide

Channel-Specific Product Content

Templates that shape one governed record for each buying channel.

Feature guide

Validation & Completeness

Category-specific rules, severities and completeness scores with clear explanations.

Feature guide

Supplier Data Ingestion

Spreadsheets, CSV, XML and JSON staged, mapped, normalized and checked before acceptance.

Feature guide

Data Lineage & Audit

Trace every value from source through transformation, approval and destination.

Capability area

Product Experience Management

See where AI Product Enrichment fits in the wider Product Experience Management capability.

Buyer FAQs

Questions evaluators ask

Will AI invent specifications?

Proposals must cite evidence, and values that do not appear in their evidence cannot be accepted without a reviewer supplying a source.

Which documents can be used as sources?

Supplier files, datasheets and manuals linked to products. Scanned-document support and specific formats are confirmed during evaluation.

Does enrichment write marketing copy?

It drafts descriptions from accepted attributes and templates. Channel-specific presentation is handled in Channel-Specific Product Content.

Can we require review for everything?

Yes. Review requirements can be set by attribute, category or confidence.

Which AI models are used?

Model selection and data handling are confirmed during evaluation, including whether your data is used for training.

Your next step

See AI Product Enrichment on data like yours.

Bring a real file, catalog or record set. We will walk through how Autonera would handle it and where your team keeps the final say.

Book a Demo