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ShelfCheck is BlueGreen\u2019s product content assurance product. It continuously audits your catalogue — titles, descriptions, attributes, variants, media, pricing and SEO fields — against the listing rules of every channel you sell on, catches catalogue chaos before customers and search engines see it, and drafts governed fixes for human approval.
Shelf audit automation is the continuous, software-driven checking of product listings against the rules each sales channel enforces — required fields, image standards, variant structure, pricing coherence and SEO basics. ShelfCheck applies it as a governed loop: audit, report, draft corrections, then human approval, so the catalogue customers see is the catalogue you intended.
Catalogue chaos rarely arrives as one dramatic failure. It accumulates: a supplier feed drops forty images on a Tuesday; a variant gains a size the channel does not recognise; a migrated description arrives as one nine-hundred-word paragraph; a meta title still names the old brand. Individually, each is small. Together they depress conversion, suppress search visibility and trigger marketplace demotions — and nobody notices until revenue dips and someone goes looking for the cause.
ShelfCheck treats listing quality as a monitored surface. Because checks run continuously against channel rules, drift is caught when it happens — before customers, marketplaces and search engines form the wrong impression of your store. That is the difference between an audit you run once a quarter and assurance your shelf is wearing all day, every day.
| Check area | What ShelfCheck verifies | What breaks without it |
|---|---|---|
| Titles & descriptions | Length, clarity, duplicated supplier copy, thin one-line descriptions | Listings that read like supplier feeds and rank for nothing |
| Attributes & variants | Required attributes present, variant structure matches channel rules | Mismatched variants, suppressed listings, unbuyable products |
| Media | Image presence, counts, aspect ratios, alt text | Blank galleries and accessibility failures that quietly kill conversion |
| Pricing | Price fields coherent per channel, sale logic consistent | Marketplace mismatches that trigger repricing and demotion |
| SEO fields | Meta titles, meta descriptions, URL slugs, structured data basics | Search engines indexing the wrong copy — or none at all |
Each product is normalised into the data spine, then evaluated rule by rule: field presence, format, media completeness, variant consistency and SEO hygiene. Failures are scored by severity, grouped into themes and written to an audit report per channel. The report is the worklist — ranked, explained and traceable back to the exact SKU.
Rules are channel-shaped because channels differ. A Google Shopping feed demands specific attribute formats; a marketplace enforces image counts and GTINs; your own storefront cares most about description quality and SEO fields. ShelfCheck applies each channel's rules to the same normalised product record, so one source of truth feeds every check and every report.
Link Shopify, WooCommerce or BigCommerce through the same connectors that power Commerce Command. ShelfCheck reads titles, descriptions, attributes, variants, media, pricing and SEO fields into one normalised view — no CSV exports, no per-channel setup.
The first pass scores every SKU against channel listing rules and produces a ranked audit report: what is missing, what is malformed, what risks demotion, and which defect themes repeat across the catalogue. Most teams find the baseline sobering — and instantly actionable.
After the baseline, checks run continuously. New products, supplier feed updates, price changes and channel rule changes re-trigger only the affected checks, so drift surfaces in hours rather than at the next quarterly clean-up.
Fix workflows draft corrections — rewritten descriptions, repaired attributes, restored alt text — and queue them for approval in the Command Centre. Nothing publishes until a human approves it, and every fix is logged in the audit trail.
Caravan and RV dealers with parts catalogues are the classic case. A parts catalogue mixes thousands of SKUs, supplier-supplied descriptions, year-and-model fitment attributes and marketplace rules — the exact environment where listing drift breeds fastest. ShelfCheck audits fitment attributes, media and description quality continuously, so the parts catalogue a customer searches at 9pm is complete. See how the whole platform fits the vertical on the caravan and RV industry page.
D2C brands use ShelfCheck to protect the shelf before launch windows and seasonal drops, when a wave of new products lands at once and every defect ships with it. Marketplace sellers point it at the channel rules that carry demotion risk — missing GTINs, image standards, variant structure — and treat the audit report as the weekly worklist, with every fix approved before it reaches the listing.
An audit report without a fix path is just anxiety. ShelfCheck closes the loop with governed fix workflows: for each finding it drafts the correction — a rewritten description, a repaired attribute, generated alt text — together with the evidence it based the draft on. Drafts queue in the Command Centre's shelf_check lane, where an operator approves, edits or rejects them.
Bulk patterns get bulk treatment. When one supplier feed breaks three hundred images, the fix workflow drafts the repair once and applies the pattern across every affected SKU — still as a single approval, still logged. That is the difference between shelf audit automation and a reporting dashboard: the work finishes. It also means catalogue quality compounds, because every fix feeds back into the next audit cycle through Commerce Command orchestration.
6 dimensions
of product data audited per SKU: content, attributes, variants, media, pricing, SEO
Continuous
monitoring after the baseline — checks re-run when catalogue or channel rules change
100%
of corrections human-approved before publication
3 platforms
supported natively: Shopify, WooCommerce, BigCommerce — plus headless via API
Shelf audit automation is the continuous, software-driven checking of product listings against the rules each sales channel enforces — required fields, image standards, variant structure, pricing coherence and SEO basics. ShelfCheck applies it as a governed loop: audit, report, draft corrections, then human approval before anything publishes.
Each product is normalised into the platform data spine, then evaluated rule by rule: field presence, format, media completeness, variant consistency and SEO hygiene. Failures are scored by severity, grouped into themes and written to a per-channel audit report. The report is the worklist — ranked, explained and traceable to the exact SKU.
Six dimensions of every SKU: titles and descriptions (length, clarity, duplication), attributes (presence and format), variant structure against channel rules, media (image presence, counts, alt text), pricing coherence per channel, and SEO fields including meta titles, descriptions and URL slugs. Checks apply per channel, because every channel enforces its own listing rules.
ShelfCheck runs on the same connectors as Commerce Command: Shopify (including Shopify Plus), WooCommerce and BigCommerce, plus headless storefronts through the API layer. The connector reads titles, descriptions, attributes, variants, media, pricing and SEO fields into one normalised view — no CSV exports, no per-channel setup.
No. ShelfCheck drafts corrections through governed fix workflows and queues them for approval in the Command Centre. Low-risk fixes can be pre-approved by rule if you choose, but by default every correction — single SKU or bulk pattern — is human-approved before it publishes, and every change lands in the audit trail.
Book a 25-minute demo. Point ShelfCheck at a store you know has shelf problems — we will run the baseline audit live and walk the report with you, defect theme by defect theme.