Fill Missing Product Data Automatically with AI Web Research

The gap nobody wants to fill by hand

Even a decent supplier file usually arrives incomplete. You get the name, the price, maybe an EAN — and blanks where the material, dimensions, technical specs, energy label or compliance data should be. Marketplaces reject listings for exactly those missing fields, and a thin product page converts worse than a complete one. So someone opens a browser, searches the manufacturer's site, finds the spec, and pastes it back in — per product, per attribute. It's slow, it's boring, and it's the step that stalls a launch.

This is the work AI web research is built to remove — turning "go look it up" into a background job.

How AI web research fills a gap

The mechanism is straightforward once the guardrails are in place:

• The AI takes a product's identifier — SKU, EAN or manufacturer model number — and the list of missing fields.
• It searches your whitelisted sources (manufacturer sites, trusted retail) for those specs.
• It writes the values back in the right format and unit, mapped to your attributes.
• Every filled value lands in a review queue, marked as researched, so nothing publishes unchecked.

Crucially, it only researches what's missing — it uses your imported data first, so you're not paying to re-fetch what you already have.

Keeping it trustworthy: whitelisting and review

Open-ended web lookup is risky — the internet is full of wrong specs and competitor pages. Two controls make it safe:

• Source whitelisting: restrict the AI to manufacturer domains and sources you trust; blacklist competitors and low-quality pages so it never learns bad data.
• Review queue with marking: each researched value is flagged as AI-sourced, so a person can confirm before it goes live — and you always know which numbers came from research versus the supplier.

These are the same guardrails that make AI enrichment reliable enough to publish.

Resolving and verifying EANs

Identifiers are a special case. AI research can look up a product's EAN/GTIN from its manufacturer code when the supplier left it blank, and cross-check an existing EAN against the product it's attached to — catching the off-by-one-digit and wrong-variant errors that otherwise surface as marketplace rejections weeks later.

Why generic ChatGPT isn't enough here

A web-enabled chat model will happily look up a single spec if you paste in the product. For a handful of items, that's fine. But there's no whitelist (so it may quote a competitor or a wrong forum post), no batching across thousands of SKUs, no mapping into your schema, and no review trail. Unreviewed, that output is exactly the kind of plausible-but-wrong data you don't want on a live listing. The value isn't the lookup — it's the controlled, auditable, bulk version of it.

How Productbay does it

Productbay's AI Autofill uses your imported data plus whitelisted web sources to fill missing attributes across thousands of products in one run. You configure which manufacturer sources are trusted (and which to block), and every researched value goes through the review queue, marked with its origin. It sits in the same flow as extraction and enrichment, so a product can come in from a supplier file, get its gaps researched, and go out complete — without a separate research task on anyone's to-do list.

This table was compiled from publicly available information. We aimed to bring transparency to the market — details may change over time. When in doubt: check both providers yourself and decide based on your own evaluation.

Web research is one stage of the pipeline; see the whole thing in AI for product data maintenance, and how completeness lifts conversion in this breakdown.

Frequently Asked Questions