
Products stay eligible, competitive, and actionable when an AI shopping agent does the researching, comparing, and buying.
Make products easier to find, compare, and buy by completing the catalog details that search engines and AI assistants need.
Turns fragmented supplier documents and manufacturer sites into complete, source-backed product records.
Retrieves missing facts from supplier PDFs, manuals, and manufacturer sites on its own, normalizes them to catalog rules, and cites a source for every value.
Search engines and AI assistants can only recommend products they can understand. In many catalogs the details buyers rely on are missing, inconsistent, or buried in supplier PDFs, manuals, and manufacturer websites: dimensions, materials, pressure ratings, compatibility, and every other attribute that decides a purchase.
New SKUs and supplier updates outpace the teams doing manual enrichment, so product discovery stays a constant effort and further investment in the buyer experience waits. The catalogs that hurt most are B2B and B2C ones where a single product carries hundreds of technical attributes and accuracy is not optional.
For each product in scope, a set of agents retrieves the missing facts from the places they already live: supplier documents, spec sheets, technical manuals, product images, and manufacturer websites. It then normalizes what it finds to the customer's catalog rules, converting values and units, mapping facts to the taxonomy, and applying attribute definitions across the products in scope.
Every value keeps its source citation and a confidence score. Conflicting or uncertain values are flagged for a person to review, against acceptance criteria and authoritative sources the customer defines. New SKUs and supplier changes run through the same rules, so enrichment keeps running as the catalog changes.
Enriched records return to the customer's product information or enterprise resource system, or to the commerce platform, through files, interfaces, or scheduled transfers, where they are stored and published as usual. The richer data also reaches the pages and feeds that external assistants and shopping agents read, which supports answer engine and generative engine optimization.
Coveo is behind this solution. Ask about enriching your product catalog with source-backed attributes, and the MACH Alliance will put you in touch with the person who can answer.
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Everyone credited on this solution
Every organization named here has a page listing the rest of its work on the Hub.

Products stay eligible, competitive, and actionable when an AI shopping agent does the researching, comparing, and buying.

Merchandisers act on performance questions without leaving their workspace, and nothing the agent proposes applies without approval.

Counter staff spend less time hunting parts, and fewer searches end with no option offered.
Contributor submission
The detail on this page comes from the contributor's own account of their own work. The MACH Alliance reviews a submission before publishing it and does not independently verify the figures in it.
What this page does not establish
Where this came from
Drawn from Coveo's submission to the Agent Solutions Hub, October 2026.
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