01 / Test
Run the public demo first.
Use synthetic resale rows or paste product-only test rows locally in your browser. No account, upload, or private file required.
How it works
Redacted rows in. Source-backed queue out.
Catalog Recall Monitor reviews product-only catalog rows against official CPSC recall records, then returns a human-readable queue: suppress recommendation, manual review, or no visible match.
Workflow
02 / Scope
Send only redacted product rows.
If the demo maps to your workflow, send 5 redacted rows free. Product title, brand, category, SKU, model, UPC, and public product URL are enough when present.
03 / Preflight
Unsafe files stop first.
Files with customer names, emails, order IDs, payment data, contracts, costs, margins, credentials, or full databases are rejected before scanning.
04 / Scan
Rows are checked against CPSC history.
Exact identifiers can support a suppress recommendation. Fuzzy title or brand evidence goes to manual review, not automatic suppress.
05 / Review
Every match gets a source link.
The report explains why each row was flagged and links to the official CPSC recall when there is visible evidence.
06 / Decide
Continue only if it changes work.
If the first free report is useful, the paid pilot is recurring monitoring for a narrow category or catalog export. No card or payment link is used before scope is confirmed.
Who handles it
Founder-operated pilot
accountableCatalog Recall Monitor is run as a concierge pilot. Early reviews are intentionally hands-on so the matching logic, report language, and buyer workflow can be checked carefully before product automation expands.
Contact is direct: ryan@catalogrecallmonitor.com.
What is not automated
bounded- No automatic product takedowns.
- No customer notification automation.
- No legal advice or compliance certification.
- No payment collection before a specific pilot scope is confirmed.
- No use of customer, order, or payment data for the first review.
Chatbot vs operating trail
A prompt can read one page. A workflow leaves evidence.
ChatGPT can be useful for one-off reading. Catalog Recall Monitor is built for repeatable catalog review: same inputs, same decision rules, official source links, private-data preflight, and an exportable report.
One-off prompt
Good for reading a recall page.
A chatbot can summarize a notice or help think through one suspicious item. That is useful, but it does not create a durable review queue.
Repeatable rules
Exact and fuzzy evidence stay separate.
Exact UPC or model evidence can support Suppress. Brand, title, or category overlap goes to Manual Review instead of pretending the answer is certain.
Source-backed artifact
The output is a report, not a chat.
Each visible match gets a reason, an action, and an official CPSC source link. The queue can be exported and shared with an operator or consultant.
Audit posture
The scan records what happened.
The demo shows input preflight, decision rules, evidence count, and artifact status. Real scans keep the same posture around source-backed review.
Decision language
The report is careful by design.
The output is decision support. It does not certify that a product is safe. For rows without visible evidence, the wording is: “No visible match found in scanned official sources as of the scan time.”
S Suppress
Hard evidence.
Used for hard identifier evidence such as exact UPC or exact model-number matches.
R Manual review
Fuzzy evidence.
Used when brand, title, category, or model text overlaps with a recall but needs human confirmation.
N No visible match
No visible evidence found.
Used when no visible match appears in the scanned official CPSC records at report time.
Low-risk first step
Try the public demo before sending anything.
The public demo shows the workflow without private data. If it maps to your catalog problem, send a small redacted sample after reviewing the data scope.
Legal boundaryThis provides source-backed decision support. It does not certify products as safe, provide legal advice, or replace human safety/compliance review.