How to choose a product data enrichment vendor

"Product data enrichment" covers at least four distinct jobs, and vendors are rarely equally good at all of them. Before you talk to anyone, decide which of these is your real problem — it changes who you should even be evaluating.

• Collection / sourcing. Pulling missing data from manufacturer sites, spec sheets, PDFs, supplier feeds, and images. The hard part is coverage and provenance, not formatting.
• Cleaning / normalization. De-duplicating, standardizing units, fixing UOM and GTIN errors, reconciling conflicting source values. The hard part is rules and edge cases.
• Structuring / attribution. Mapping raw values into your taxonomy and attribute schema, filling required fields per category, categorizing SKUs correctly. The hard part is your schema, not theirs.
• Generation. Writing titles, descriptions, feature bullets, and channel-specific copy, plus translation/localization. The hard part is accuracy and brand voice, not fluency.

A vendor that nails generation but can't source missing specs will leave you with beautifully written listings built on the gaps you already had. Rank these four by where your pain actually is, and weight your evaluation accordingly.

Fluent and wrong is the failure mode that matters. A model will happily output "316 stainless steel" for a 304 part, or invent a thread pitch, because the text reads correctly. In B2B especially, a wrong attribute is worse than a blank one — it drives returns, rejected POs, and warranty claims.

Demand evidence, not adjectives:

• Per-attribute accuracy on YOUR data, measured against a human-graded gold set you control — not the vendor's benchmark on their categories.
• Source citation / provenance. Every enriched value should trace back to where it came from (a specific spec sheet, page, or supplier field). "The model knows" is not provenance. This is what lets your team spot-check instead of re-verifying everything.
• Confidence scoring with calibration. A confidence number is only useful if low-confidence actually correlates with wrong. Ask to see a precision/recall curve, or build one in the pilot.
• Abstention behavior. The right answer to "I can't determine the flash point from available sources" is to leave it blank and flag it, not to guess. Hallucination-by-default is disqualifying. Test this explicitly.

If a vendor can't or won't let you measure accuracy on your own SKUs before you sign, treat that as the answer.

Enrichment quality is bounded by what the vendor can find. Two vendors with identical models produce wildly different results based on what sources they can actually reach and parse.

Probe these:

• Source breadth. Manufacturer sites, distributor catalogs, spec-sheet PDFs, images (OCR + vision), supplier feeds, GS1/GDSN, industry databases. Ask which they pull from automatically vs. require you to supply.
• PDF and image extraction. Most real spec data lives in ugly PDFs and on-product label images. This is where weak vendors fall apart. Hand them your messiest spec sheet during the pilot.
• Category depth. A vendor strong in apparel may be naive about electrical, plumbing, MRO, chemicals, or electronics — categories with deep attribute schemas, compatibility relationships, and regulatory fields. Ask for references in YOUR vertical.
• Coverage rate, not just accuracy. A vendor that's 98% accurate but only fills 40% of required attributes may be less useful than one that's 94% accurate at 85% fill. Measure both; they trade off.
• Long-tail and net-new SKUs. How does it handle a brand-new product with almost no public footprint? That's the hardest and often most valuable case.

This is the criterion most buyers skip, and it's increasingly the one that decides whether enrichment pays off. Filling your attribute schema is table stakes. The question that drives revenue is: does the enriched data match how buyers — and the AI systems they now use — actually search, compare, and decide?

A correctly-filled "material: nitrile" field is useless if your buyers search "oil-resistant gloves" and the engine never connects the two. Modern product discovery runs through marketplace search, faceted filters, Google's product feeds, and increasingly LLM-driven shopping assistants that match on structured attributes and natural-language intent.

Ask vendors:

• Do they enrich toward a fixed schema only, or also toward buyer-facing search terms, synonyms, and use-cases?
• Can they score a SKU's completeness against what's required to rank and get chosen on your priority channels (Google UCP/feed specs, marketplace category requirements, your on-site facets)?
• Do they capture the comparison attributes buyers use to choose between two similar SKUs, not just the catalog attributes?

This is exactly the gap Anglera was built to close — it scores and fills every SKU against buyer signals (how the buyer searches, compares, and decides), not just against a static template. If discovery and conversion are your goal rather than internal tidiness, weight this heavily.

Enriched data that lives in a vendor's portal is half a solution. The expensive, overlooked work is getting it back into your systems cleanly and keeping it in sync.

• Write-back to your source of truth. Most teams already have a PIM (or ERP/MDM) as the system of record. The vendor should write enriched, structured data back into it — your PIM stores the data, the enrichment layer does the work of filling it. Confirm bidirectional sync, not a one-way export.
• Schema mapping. Can it map to YOUR taxonomy and attribute model, including category-specific required fields and value lists, or does it force its own?
• Connectors vs. CSV. Native connectors to your PIM (Akeneo, Salsify, inriver, Pimcore, etc.), commerce platform, or ERP beat a quarterly CSV swap. Ask what's pre-built vs. custom.
• Incremental and event-driven runs. New SKUs and changed SKUs should trigger enrichment automatically. A system that only does big batch reruns will drift.
• Round-trip integrity. Units, encoding, multi-value fields, and language variants survive the round trip without corruption. Test this — it's where silent data loss happens.

Note what enrichment is NOT: it sits alongside your PIM and CRM, it doesn't replace them. Be skeptical of any vendor pushing you to make their tool your system of record.

No enrichment system is 100% right, so the operating model around the automation matters as much as the model itself. The best setups make human review cheap and targeted instead of total.

Look for:

• Review queues driven by confidence and risk. Low-confidence values and high-stakes fields (regulatory, safety, compatibility) route to a human; high-confidence routine fields flow through. This is what makes scale economical.
• Audit trail. Who or what changed each value, when, from which source. Essential for regulated categories and for debugging accuracy regressions.
• Feedback loop. When your reviewer corrects a value, does the system learn from it for similar SKUs, or will it make the same mistake 500 more times?
• Role-based access and approvals for teams where merchandising, data, and compliance all touch the catalog.
• Rollback. Can you revert a bad enrichment run? You will need to at least once.

Pricing in this space is all over the map: per-SKU, per-attribute, per-enrichment, per-seat, or flat platform fees. The sticker price is rarely the real cost.

Watch for:

• What a "unit" means. Per-SKU sounds clean until you learn a re-enrichment, a translation, or each attribute counts separately. Model your actual catalog size and refresh cadence.
• Re-runs and updates. Catalogs change constantly. If every refresh is billed like net-new, your year-two cost can dwarf year one.
• Internal cost. A cheap tool that needs two FTEs babysitting review queues isn't cheap. A pricier tool with strong automation and review tooling can be the lower total cost.
• Implementation time and pro-serv fees. Ask for a concrete timeline. Mature vendors land production value in roughly 30 days, not two quarters. A long, costly onboarding is a signal.
• Security and data handling. SOC 2, where your data is processed, whether your catalog trains shared models, and data residency if you're international.
• Exit terms. You own the enriched data and can export it in a usable, mapped format if you leave. Get this in writing.

Never buy on a demo. Run a structured pilot on a representative slice of YOUR catalog and grade it like an experiment.

1. Pick 300–1,000 SKUs that include your hard cases — sparse net-new products, ugly PDF specs, a deep-attribute category, and a few you know the right answers to cold.
2. Build a gold set. Have your own expert correctly fill 50–100 of them by hand, blind to the vendor's output.
3. Measure four numbers: per-attribute accuracy, coverage/fill rate, hallucination rate (confident-and-wrong on your gold set), and the share of values with usable provenance.
4. Time the round trip. How long from raw input to correctly-mapped data back in your PIM, including review?
5. Stress the edges. Feed it a SKU with conflicting sources and one with almost no public data. Watch whether it abstains or invents.
6. Score and compare. Weight the criteria by your priorities from section one, and let the pilot numbers — not the demo — decide.

Run the same pilot against two or three vendors with the identical SKU set and gold standard. Apples-to-apples on your data is the only comparison that predicts production results.