Shopify product feed on autopilot:
categorised, described, kept current
We audited a live Shopify store and found 81% of products sitting outside any category, no reviews wired in and missing regulatory numbers, the kind of feed rot that happens gradually as a catalogue grows past what one person can maintain by hand. We build enrichment that fixes this once and keeps it from drifting back.
The process today
A Shopify catalogue that grows product by product, added whenever there is time, tends to drift: some products get a full category and description, others get added quickly during a launch rush and never get finished. Months later, a meaningful share of the catalogue sits outside any real category structure, invisible to on-site search filters and harder for customers to browse.
The second cost is inconsistency in descriptions: some products have a full, well-written description, others have a single line copied from a supplier spec sheet, and a customer comparing two similar products sees a store that looks unfinished on one side.
The third is compliance drift in regulated categories, supplements, cosmetics, anything with required disclosures, where a missing regulatory number is invisible until a platform or a regulator flags it, usually at the worst possible time.
What the agent does
The agent audits the current catalogue against your actual taxonomy, flags every product missing a category, a required attribute, or a complete description, the kind of audit that found 81% of a real store’s catalogue outside any category before we fixed it. It then categorises against rules tuned to your store, drafts missing descriptions from your existing product data and supplier copy, and fills attribute gaps like size, material or compliance fields.
Every AI-assisted description is reviewed by a person before it replaces a live listing, so nothing publishes that oversells a product or invents a spec. Once the backlog is cleared, an ongoing check runs on new products as they are added, catching the same drift before it builds up again.
Typical setup: Shopify’s Admin API and product feed, your taxonomy and supplier data, and a review queue for AI-drafted copy.
Rollout follows the same sequence across every automation we build: map the real process together with the people doing the work today, including the exceptions and the real volume, not just the clean-path version; build and test against a sample of your real data rather than a demo dataset; run a dry run against live activity before anything is allowed to act on its own; then hand over the logs, the kill switch and a short written guide so your team can run it without us in the room. The 30-day tuning window that follows launch is treated as real work, not a formality: thresholds, wording and edge cases get adjusted against what the first weeks of real usage actually show, not against what looked right before launch.
What stays with humans
Approving every description before it goes live, and any decision about product claims that touch regulatory or compliance language, stay with your team. The agent drafts and categorises from what it is given; it does not add a claim or a specification that is not already documented somewhere in your product data.
Guards
No AI-drafted description or attribute change publishes without a human review step. The categorisation rules run against a sample of your real catalogue before touching the live feed, and a before-and-after report shows exactly what changed. A kill switch reverts the ongoing check in one message if a new product rule misfires.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $700 | One store, categorisation cleanup, description drafts, attribute enrichment | 5 to 10 days |
| Department package | from $2,500 | Full catalogue rebuild into new product lines, ongoing feed governance | 2 to 4 weeks |
Running cost is usually $20 to $100 a month in model usage depending on volume, with a budget cap set before launch.
Related
Pair this with shopify order tagging and workflows and product descriptions at scale, and see image alt text and tagging for a related process. The full package breakdown is on the AI agents service page and the automation-everything overview; for a real build behind this pattern, see the d2c store thailand audit and rebuild case study and .
Ready to see what this looks like for your stack? Get in touch and we will map the integration in the first call.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does Shopify feed enrichment cost?
From $700 for an audit and cleanup of categorisation and core attributes, live in 5 to 10 days. A full catalogue rebuild into new product lines, the kind we did for a sports nutrition brand's Shopify store, sits in our e-commerce package.
What did you actually find auditing a live store?
On one real audit, 81% of products sat outside any category, there were no product reviews live anywhere on the site, and several listings were missing the regulatory numbers required for that product category. We fixed the categorisation and flagged the rest for the client to action.
Does the AI write descriptions from nothing?
No, it drafts from your existing product data, specs and any supplier copy you provide, and a person reviews every draft before it replaces a live listing. It does not invent specifications that are not in your source material.
How do you stop the feed from drifting back into disorder?
An ongoing check runs on new products as they are added, flagging anything missing a category or required attribute before it goes live, instead of relying on someone remembering to categorise it manually.
Will this affect our SEO?
Consistent categorisation and complete attributes generally help product and category page SEO, since search engines and on-site search both rely on that structure, but we do not promise a ranking outcome.