What it is
A lightweight catalog diagnostic for AI-facing product data, with an optional paid findings report when you need a saved issue register and cleanup order.
Is your product data ready for AI-facing commerce?
Check whether missing fields, weak identifiers, variants, prices, availability, or feed structure are limiting AI-facing commerce surfaces.
Paid report
$150 USD$99 USD
Use code LAUNCH99 at checkout for $51 USD off the $150 USD standard price; the paid Catalog Scanner report is $99 USD.
The free scanner preview remains free.
Merchants with a product export, feed sample, API payload, or catalog file.
What it solves
Missing fields, messy product details, weak identifiers, stale price or availability values, and target-format issues that make product facts harder for AI search tools and shopping agents to trust.
Who it is for
Merchants who already have a feed export, product file, API payload, or catalog sample they want checked.
Deliverables
- Catalog Scanner result
- Issue mix and severity
- Optional paid PDF findings report
- Recommended AI-facing product-data cleanup path
What is not included
- Silent unsupported value filling
- Claims that one converted feed equals full readiness
- Storefront theme fixes or custom protocol implementation by default
Timeline
Starts
Report packaging starts after checkoutExpected result
PDF access expected within one dayCatalog Scanner uses the saved rules-based catalog result rather than making you wait on a manual audit queue. Most reports are available shortly after payment confirmation; the one-day window covers payment sync and delivery buffer.
- Rules-based catalog result is rebuilt from saved source evidence
- PDF findings report is attached to the account
- Download access appears after payment confirmation
- Inferred values and unsupported gaps remain clearly flagged
If you have any questions before or after purchase, please don't hesitate to reach out at support@autonomouspath.ai.
Launch version
The sequence can start two ways: run the free Catalog Scanner preview first, or go straight to the paid Catalog Scanner report when you already have a source catalog ready. Either path keeps the report tied to catalog evidence before cleanup scope starts.
Improve readiness before heavier work.
This service improves readiness, fit, and the quality of the next decision for AI search and agentic commerce. It does not promise platform activation, approval, rankings, or guaranteed results.
Sample paid report
See what the paid Catalog Scanner report includes.
Open a sample PDF before buying the paid findings layer. It shows the report structure generated from a saved catalog snapshot: normalization coverage, target-fit validity, inferred-value flags, recurring lint categories, and cleanup order.
Saved snapshot
Lint categories
Inferred values
PDF delivery
- Catalog snapshot summary with record counts, source type, and target focus.
- Normalization coverage, target-fit validity, and inferred-value flags.
- Recurring lint categories with record-level cleanup context.
- Recommended cleanup order and limits on what the catalog report proves.
Sample data is illustrative. A paid Catalog Scanner report documents source catalog evidence only; it does not change records, guarantee Merchant Center acceptance, or prove full storefront readiness.
How the steps fit
Use the Catalog Scanner before the paid report.
Catalog Scanner is the smaller free step. The paid Catalog Scanner report is the findings layer. Catalog Remediation and Recurring Monitoring come after that.
Use the free site preview when the AI-commerce next step is still unclear.
Site Scanner checks a sample of pages, policy coverage, behind-the-scenes product data, and one recommended next move for AI search and shopping-agent readiness.
- Best when the team is still unsure whether the issue is the site, trust, structured data, or platform path.
- Scores what the bounded scan can actually verify on the storefront.
- Recommends the safest next service without asking you to choose a technical path first.
Use the smaller feed check when catalog cleanup might be the real issue.
Upload or paste a representative feed sample to see whether missing fields, inferred values, or weak catalog structure are slowing AI-facing product data down.
- Best when a feed export, Merchant Center sample, or catalog file is already in hand.
- Checks product records, fit for the selected path, and common feed issue categories.
- Keeps feed questions separate from storefront, policy, and pathway claims.
Start paid work only after the issue is clear.
Move into the paid full report when the site needs a deeper review. Move into Catalog Remediation when catalog data is clearly the issue, or AI Search Remediation when site clarity is the issue.
- Site Scanner for broader storefront coverage and paid report delivery.
- Catalog Scanner for a saved PDF findings layer before AI-facing cleanup work.
- AI Search Remediation, Catalog Remediation, and Recurring Monitoring come after confirmed findings.
Proof point
A sample result from a realistic messy export.
Messy merchant export routed into AI-facing feed cleanup first.
This proof card is based on the repo's realistic `messy-merchant-export.csv` fixture against the Google-side feed path. It shows the kind of rules-based output Catalog Scanner surfaces before any paid Catalog Scanner report scope is proposed.
The sample is workable, but not ready to push forward cleanly.
- 4 source records parsed from the repo's messy sample export
- 3 records cleaned into a shared product shape
- 1 record structurally usable for the Google-side target check
- 7 feed issues total: 6 major and 1 moderate
Catalog Scanner separates feed cleanup from broader AI search readiness claims.
- Top recurring issues: image links weak or broken, inferred values required, and price cleanup.
- Recommended next service: Catalog Scanner.
- Guardrail: this sample does not claim Merchant Center approval or broader AI search readiness.