Product data enrichment
Product data that AI engines recommend.
Product data enrichment turns incomplete supplier files, ERP exports and spec sheets into complete, structured and bilingual product content. We do it with AI and human review, publish it to Akeneo PIM or directly to Shopify, and structure it so ChatGPT, Perplexity and Google AI Overviews can find and recommend your products.
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Why it matters
Why does product data decide whether AI recommends you?
AI assistants answer buyer questions from structured, complete and consistent product information. When a specification is missing, contradicts another source or sits inside a PDF, the assistant recommends a competitor whose data is clearer.
Most B2B catalogues were built for a price list, not for search. Attributes are incomplete, descriptions are copied from suppliers, French content lags behind English, and nobody can say which spreadsheet holds the current version. That is the gap this service closes.
The buyers are already there: Shopify reported that AI-referred buyers converted 80% better and placed 14% larger orders in Q2 2026. Every B2B SKU is a specialized product, and specialized products are where AI search sends its traffic.
The maturity model
Where does your catalogue stand, from Stage 0 to Stage 3?
Product data is never finished, so we give every product page a maturity stage and move the ones that matter first. The target is Stage 3 for your top 50 sellers, which then set the standard for the rest of the catalogue.
- Stage 0
Identifiable
A SKU, an image and a price. Most B2B catalogues start here.
- Stage 1
Searchable
Core attributes are structured, so filters and site search work.
- Stage 2
Sellable
Full description, verified specifications and real downloads.
- Stage 3
Agent-ready
Content grounded in source documents, cited, and audited every month.
How it works
How do you turn messy supplier data into products AI engines cite?
Five steps, one team. AI does the heavy lifting, people check the work, and your team approves what goes live.
- 01
Source
ERP exports, supplier spreadsheets, spec-sheet PDFs, manufacturer sites and certification records. Send what you have and we find the rest.
- 02
Validate
We cross-check sources, flag conflicts and keep a citation for every attribute. AI fills gaps only from your own and your manufacturers’ files, and never writes a link.
- 03
Enrich
Category tree, structured specs, then titles, descriptions, FAQs and comparisons in English and French.
- 04
Publish
Into Akeneo as your single source of truth, or straight into Shopify metafields and metaobjects.
- 05
Get found
Product schema, llms.txt and complete attributes, then monthly tracking of how often AI engines cite you.
Where your data lives
Should enriched product data live in Akeneo or in Shopify?
It depends on catalogue complexity, not size alone. We implement both, and we tell you when you don’t need a PIM.
Akeneo PIM
One source of truth for complex catalogues
- Many technical attributes per product family
- Several channels: Shopify, distributors, marketplaces, print
- Teams that need approval workflows and completeness scoring
Shopify direct
Structured data, no PIM to run
- Shopify is your main sales channel
- Specs modelled in metafields and metaobjects
- Faster to launch, no extra licence
Proof
What happened when an electrical distributor tried it?
Its product pages started at Stage 0: an image, a price and little else. A small AI-assisted workflow added more than 81,000 data points to the PIM. The first automation looked complete, but its PDF links were invented, so we rebuilt it to work only from manufacturer files, with a named data owner from day one. Wrong electrical data is a safety issue, not a typo. The same method governs enterprise catalogues like Goodyear’s North American Akeneo PIM (via Publicis Sapient).
Read the Goodyear case study →From the stage
AI year one: what we tried, what failed, what’s next
Rudy Abitbol presented this session at B2B eCommerce World Americas in Indianapolis on November 2, 2026: one electrical distributor’s first year of AI on product data, the Stage 0 to 3 model and five actions to take before 2027. The slides are free to download.
Watch the session and get the slides →FAQ
You asked, we answered.
What is product data enrichment?
Product data enrichment is the process of completing and improving product information: filling missing attributes, standardizing units and naming, classifying products into a category tree, and writing titles, descriptions and FAQs. Human After All combines AI research with human review, then publishes the result to Akeneo PIM or Shopify.
How long does it take to get up and running?
We never fix the whole catalogue at once. We start with your top 50 sellers and bring them to Stage 3, with one gold-standard product page per category that your team approves. Then we roll out category by category and re-audit every month, so enrichment becomes a routine, not a one-time project.
How does it connect to my ERP, PIM or data provider?
We work with what you run. ERP data comes from SAP, NetSuite, Microsoft Dynamics, Tecsys, Odoo, Acomba or ServiceCentre through connectors we build. With Akeneo, enriched content lands in the PIM and syncs to Shopify. Without a PIM, we publish straight into Shopify metafields and metaobjects. An existing enrichment provider becomes one more source.
How do you enrich product data?
We combine what you give us with what we find: manufacturer documentation, spec sheets, distributor catalogues and certification records. AI cross-references the sources, flags conflicts and keeps a citation for every attribute. Our team reviews the output, and yours approves what goes live, in English and French.
How many SKUs can you handle?
From a few hundred SKUs to large technical catalogues. Size alone does not decide the setup, complexity does: many attributes, channels or languages point to Akeneo, while a focused catalogue sold mainly on Shopify can live in Shopify.
Can I try it before committing?
Yes. Start with the free GEO Audit to see whether ChatGPT, Claude and Gemini name your company when buyers ask, then run a paid pilot on your top 50 sellers. You keep the enriched data either way.
How do you stop AI from inventing product data?
AI only fills gaps from source documents: your files and your manufacturers’ documentation, retrieved and cited. It never generates links or specifications on its own, every attribute carries its source, and a named data owner on your side approves what goes live. In regulated categories like electrical products, that is a safety requirement.
How do you measure AI visibility?
We ask ChatGPT, Claude, Perplexity and Google AI the questions your buyers ask about your top SKUs, track how often your products and pages are named and cited, and report it monthly. We measure discoverability, not just conversion.
See where your catalogue stands.
Tell us about your catalogue and your platform. We’ll show you which stage your top sellers are at and what it takes to reach Stage 3. In English or French, from Montréal.