How to enrich product data at scale (100,000+ SKUs)

At scale you can't eyeball quality, so the first deliverable is a target schema, not a content batch. For each product category, write down the attributes a complete record must carry and mark each one required or optional. A power tool needs voltage, battery platform, chuck size, no-load speed, and a UL/CSA flag; a men's jacket needs fill type, shell material, fit, care, and packable dimensions. A single global schema across all categories is the most common scaling mistake — it forces every product into the same thin set of fields and guarantees you're under-describing most of the catalog.

Then turn the schema into a completeness score you can compute per SKU and roll up per category:

• Completeness — what percent of required attributes are populated, weighted so the buyer-critical fields count more than nice-to-haves.
• Validity — does each value pass its type and range check (a 9,000V drill is a typo, a GTIN that fails its checksum is wrong, "Blue/Navy" in a single-value color field is unnormalized).
• Accuracy — does the value match the source of truth. This is the one you can only measure against a verified sample, which is why you need a gold set (covered below).

The point of scoring isn't a vanity dashboard. It's that at 100,000 SKUs you will never review every record by hand, so you need a number that tells you which 8,000 are below bar and which attributes are systematically missing. Enrichment without a score is just typing into a void.

A complete record and a useful record are different things. You can populate every field on a datasheet and still lose the sale because none of those fields are how the buyer actually searches, compares, or decides. At scale this matters more, not less, because the marginal SKU is the one nobody hand-curated.

Work backwards from the buyer:

• How they search. The terms, filters, and synonyms a buyer types — "impact driver 20V brushless," not the manufacturer's internal model family. Your attribute values have to match the facets your channels and search engines filter on.
• How they compare. The two or three specs that decide between near-identical SKUs — flow rate, NEMA rating, compatibility, throughput. If those are blank or inconsistent across competing SKUs, the buyer can't choose you.
• How they decide. Compliance flags, warranty, in-the-box contents, compatibility with what they already own. Returns data is gold here — the attribute people return items over is the attribute your listing failed to make clear.

This is the difference between scoring a SKU as "95% of fields filled" and scoring it as "answers the questions a buyer brings." Increasingly the reader isn't even human: AI shopping assistants and agentic checkout flows match on structured attributes wherever they find them, and a record that's complete-but-not-buyer-shaped gets surfaced less or described wrong. Scoring against buyer signals is the part that turns enrichment from tidiness into revenue, and it's the lens Anglera builds its scoring around.

Treating all 100,000 SKUs as equally urgent is how enrichment programs stall. They almost never are. Segment first, then sequence the work so value lands early and you can ship in waves instead of a year-long big bang.

Score each SKU on two axes and act on the matrix:

1. Value — revenue, traffic, margin, strategic lines, or items in active campaigns. The usual Pareto holds: a minority of SKUs drives the majority of demand.
2. Gap size — how far below the completeness bar the record is today.

Then:

• High value, big gap — do these first. Highest return per hour of work.
• High value, small gap — quick wins; close the last few attributes and move on.
• Low value, big gap — batch-automate with light review; don't spend analyst time here.
• Low value, small gap — leave them; revisit only if they start getting traffic.

A practical sequencing tactic: enrich by category cluster, not alphabetically or by SKU ID. Products in the same category share a schema, the same source documents, and the same normalization rules, so your per-SKU cost drops sharply once a category's pipeline is dialed in. Knock out your top revenue categories end to end, prove the quality bar holds, then fan out. Waves also give you a feedback loop — you learn where the automation is weak before you've spent the whole budget.

Enrichment is mostly an extraction-and-reconciliation problem, not a writing problem. The attributes you need already exist — they're just scattered across formats no one wants to read by hand. At scale, build ingestion that pulls from every source and records where each value came from.

Common sources, roughly in order of trustworthiness:

• Manufacturer / brand data — spec sheets, datasheet PDFs, brand portals, official product pages. Highest authority; hardest to parse because it's PDFs and HTML, not clean feeds.
• Supplier and distributor feeds — CSV/Excel exports, EDI, GS1/GDSN data pools. Structured but messy: every supplier names the same attribute differently.
• Existing internal systems — your ERP, current PIM, past listings. Useful but often the source of the rot you're trying to fix.
• Reference and standards data — GS1 GTIN registries, category taxonomies (Google Product Taxonomy, UNSPSC, industry schemas), regulatory databases.
• Unstructured and derived — images (for OCR'd specs and attribute extraction), customer reviews (for real-world use cases and missing attributes), competitor listings (for completeness benchmarks, used carefully).

The non-negotiable at scale is provenance: store, per attribute, where the value came from and when. When a manufacturer datasheet and a supplier feed disagree on chuck size, provenance is what lets you resolve it by rule ("manufacturer datasheet wins over supplier feed") instead of by hand, and it's what lets you re-verify later without re-deriving everything. No provenance, no trust, no automation.

Raw sourced data is inconsistent by default, and generation built on un-normalized inputs produces confident garbage. Normalization is the unglamorous middle of the pipeline that makes everything downstream work.

The core operations:

• Unit and format standardization. Convert everything to canonical units (mm vs in, kg vs lb, V vs v), fix number/date formats, and standardize enumerations so "S.S.," "stainless," and "304 SS" collapse to one value.
• Taxonomy and attribute mapping. Map every supplier's field names to your schema. "Colour," "Color," and "Finish" may all feed one attribute — or may need splitting. This mapping is reusable per supplier, so it's a one-time cost per source, not per SKU.
• De-duplication and matching. Identify the same physical product arriving from multiple suppliers under different SKUs. Match on GTIN where present, fall back to fuzzy matching on brand + MPN + key attributes. Getting this wrong creates phantom variants that fracture your catalog.
• Validation and repair. Run GTIN checksum validation, range checks, and required-field checks. Flag — don't silently fill — anything that fails, so a human or a higher-authority source resolves it.
• Conflict resolution. When sources disagree, resolve by your provenance-based authority rules, and log the decision.

Do this category by category and the rules compound: the normalization you build for your first power-tool supplier mostly carries to the next one. Skip it and your automated generation will faithfully amplify every inconsistency 100,000 times.

This is the step people mean when they say "enrich," and it's where you choose your throughput-vs-accuracy point. Three techniques, used together:

• Deterministic rules for anything derivable — shipping weight from dimensions and material, compliance flags from category, derived attributes from known specs. Cheap, exact, auditable. Use these wherever you can.
• Extraction models to pull structured attributes out of PDFs, images, and HTML. This is where the volume lives — turning a datasheet into populated fields.
• LLM generation for the genuinely generative work: titles, descriptions, feature bullets, category-appropriate copy at scale. Powerful and dangerous in equal measure, because an LLM will invent a torque spec as fluently as it reports a real one.

The guardrails are what separate a pipeline from a hallucination machine:

1. Ground generation in sourced data. Generate from the normalized attributes and source documents you already verified — never from the model's parametric memory. A description should only assert specs that exist in the record.
2. Attach a confidence score to every generated value, and set thresholds: auto-accept high confidence, route medium confidence to human review, reject and re-source low confidence.
3. Keep humans on the exceptions, not the volume. The economics only work if review scales sublinearly — analysts handle the flagged minority and the high-value SKUs, while the long tail flows through automatically. A reviewer approving batches with spot-checks can cover thousands of SKUs a day; a reviewer typing every field covers dozens.
4. Never auto-publish unverifiable claims. Safety, compatibility, regulatory, and dimensional specs that can cause a return or a liability should clear a higher bar than marketing copy.

Build vs. buy lands here. Building gives you control and no per-SKU vendor cost but means owning extraction models, prompt pipelines, evals, and the review tooling — a real engineering investment. Buying or using a service like Anglera trades that for faster time-to-value and a pipeline that's already instrumented. The honest tradeoff is control and long-run cost versus speed and maintenance burden; pick based on whether enrichment is core engineering for you or a capability you want delivered.

Completeness and validity you can compute automatically. Accuracy you cannot, because a confidently wrong value passes every format check. The answer at scale is statistical, not exhaustive.

• Build a gold set. Have experts manually verify a representative sample — a few hundred to a couple thousand SKUs spanning your categories and your hardest cases. This is your ground truth.
• Measure the pipeline against it. Run your automation over the gold set and compute precision and recall per attribute type. Now you know, with numbers, that voltage extraction is 99% accurate but material inference is 84% — so you auto-publish the first and route the second to review.
• Sample continuously in production. Pull a random sample from each batch and audit it. Accuracy drifts as suppliers change formats and models change behavior; continuous sampling catches regressions before they reach 50,000 SKUs.
• Close the loop on errors. Every error a reviewer or customer catches becomes a new gold-set case and, ideally, a new rule or eval. The pipeline should get measurably better over time, not just bigger.

The discipline here is what lets you trust automation on six figures of SKUs without reading them all. You're not claiming the machine is perfect — you're proving, with a measured error rate per attribute, that it's good enough to auto-accept where the stakes and the accuracy both justify it, and humble enough to escalate where they don't.

Where the enriched result lands determines whether the work compounds or repeats. The expensive mistake at scale is enriching at the feed or channel layer — transforming data on its way out to each marketplace. That fixes the projection, not the product: the enrichment never writes back, so your source of truth stays thin, and you redo the same work on every one of a few hundred channels, forever.

Do the opposite:

1. Write enriched, verified records back into your single source of truth — PIM, ERP, commerce platform, or even a governed flat file if you don't have a system yet. Once.
2. Let your feed and syndication tools do what they're genuinely good at — translating that one clean record into each channel's format and delivering it. Every channel, marketplace, and AI assistant then inherits the same complete record, including surfaces you don't control and haven't connected yet.
3. Run enrichment as a continuous loop, not a one-time project. Ingest, clean, enrich, verify, write back — then monitor and re-enrich what underperforms or goes stale. New SKUs arrive, suppliers change, channel rules shift, and search behavior moves. A catalog enriched once as a project drifts back to thin within a couple of quarters; a loop keeps quality compounding.

This is exactly the line Anglera draws — your PIM stores the data; Anglera does the work — running the gather/clean/enrich/score loop upstream and writing the result back to your source of truth, with typical implementation around 30 days. Whether you build it or buy it, the architecture is the same: do the work once, upstream, where every surface reads from it.