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TECHNICAL & SCHEMA

All of your product data, readable by machines.

All of your product data, readable by machines.

Searchpanel parses every product page the way an engine does, validates the structured data field by field, and reports the markup, canonical, and llms.txt problems that stop a product from being read at all.

Searchpanel parses every product page the way an engine does, validates the structured data field by field, and reports the markup, canonical, and llms.txt problems that stop a product from being read at all.

Audit your products free

Audit your products free

Audit your products free

Audit your products free

Scored across the answer engines your buyers actually use

Scored across the answer engines your buyers actually use

[ WHAT WE CHECK ]

Everything that decides whether a machine can read you.

Everything that decides whether a machine can read you.

Product schema, review markup, canonical tags, index status, llms.txt, and machine-readable specifications, checked on every tracked product page.

Product schema, review markup, canonical tags, index status, llms.txt, and machine-readable specifications, checked on every tracked product page.

Product schema

Whether a Product block exists, validates, and carries the fields engines need to match a SKU to a real item.

Review markup

Whether your ratings are expressed as valid aggregateRating markup rather than text a parser has to guess at.

Canonical and indexing

Which URL you declare as canonical, and whether the page is indexed on the surfaces engines draw from.

llms.txt

Whether engines have a machine-readable file describing your catalogue, and which products it leaves out.

Machine-readable specs

Specifications written in prose that no parser can extract into a field an engine could filter on.

See what an engine can read on your pages.

Run a free audit and get the schema, canonical, and llms.txt problems on your best sellers, each with the exact field and line.

Audit your products free

Audit your products free

Product schema

Whether a Product block exists, validates, and carries the fields engines need to match a SKU to a real item.

Canonical and indexing

Which URL you declare as canonical, and whether the page is indexed on the surfaces engines draw from.

Machine-readable specs

Specifications written in prose that no parser can extract into a field an engine could filter on.

Review markup

Whether your ratings are expressed as valid aggregateRating markup rather than text a parser has to guess at.

llms.txt

Whether engines have a machine-readable file describing your catalogue, and which products it leaves out.

See what an engine can read on your pages.

Run a free audit and get the schema, canonical, and llms.txt problems on your best sellers, each with the exact field and line.

Audit your products free

Audit your products free

[ HOW A PAGE IS READ ]

Tools to help you see the page the way an engine does.

Tools to help you see the page the way an engine does.

Every page is fetched, parsed, and validated field by field, so a technical problem is a specific broken line with a location rather than a warning icon on a dashboard.

Every page is fetched, parsed, and validated field by field, so a technical problem is a specific broken line with a location rather than a warning icon on a dashboard.

WHAT AI SEES

See your page the way an engine parses it.

Every product page is fetched and parsed the way an engine does, so you see the fields it actually extracted rather than the page a browser renders for a person.

  • Parsed from the live page
  • Extracted field by field
  • Set against what the answer needed

VALIDATION

See exactly which field is broken, and why.

Each error names the field, the value found on your page, the value the specification requires, and where it sits, so a fix is a line change rather than an investigation.

  • The field and its location
  • Found value against required
  • Which engines it affects

CATALOGUE COVERAGE

See which products are readable, and which are not.

The same checks run across your whole catalogue, so a template problem shows up as a count rather than as the one product you happened to open.

  • Counts across every tracked SKU
  • Grouped by page template
  • Movement since your last deploy

2,847

buyer prompts tracked per brand, scored every day

5×

average lift in AI visibility within the first ninety days

+32%

higher conversion on sessions that arrive from an AI recommendation

2,847

buyer prompts tracked per brand, scored every day

5×

average lift in AI visibility within the first ninety days

+32%

higher conversion on sessions that arrive from an AI recommendation

2,847

buyer prompts tracked per brand, scored every day

5×

average lift in AI visibility within the first ninety days

+32%

higher conversion on sessions that arrive from an AI recommendation

How Elcove became the #1 serum ChatGPT recommends.

Elcove ranked well on Google but was invisible in AI answers, a rival was named for every “gentle vitamin C” prompt. SearchPanel scored all 312 SKUs, surfaced the title and A+ gaps on their hero serum, and queued the fixes.

Six weeks later, that serum was the first product named across ChatGPT and Rufus and the wins rolled out across the catalogue.

Dana Lewis

VP Growth · Elcove

Hero SKU visibility

31 → 79

▲ 48

Share of voice

9% → 38%

Revenue from AI search

3.1× in 90 days

How Elcove became the #1 serum ChatGPT recommends.

Elcove ranked well on Google but was invisible in AI answers, a rival was named for every “gentle vitamin C” prompt. SearchPanel scored all 312 SKUs, surfaced the title and A+ gaps on their hero serum, and queued the fixes.

Six weeks later, that serum was the first product named across ChatGPT and Rufus and the wins rolled out across the catalogue.

Dana Lewis

VP Growth · Elcove

Hero SKU visibility

31 → 79

▲ 48

Share of voice

9% → 38%

Revenue from AI search

3.1× in 90 days

How Elcove became the #1 serum ChatGPT recommends.

Elcove ranked well on Google but was invisible in AI answers, a rival was named for every “gentle vitamin C” prompt. SearchPanel scored all 312 SKUs, surfaced the title and A+ gaps on their hero serum, and queued the fixes.

Six weeks later, that serum was the first product named across ChatGPT and Rufus and the wins rolled out across the catalogue.

Dana Lewis

VP Growth · Elcove

Hero SKU visibility

31 → 79

▲ 48

Share of voice

9% → 38%

Revenue from AI search

3.1× in 90 days

FAQS

Frequently asked questions

Frequently asked questions

Still deciding? Run the free audit and see your own numbers before you talk to anyone.

Still deciding? Run the free audit and see your own numbers before you talk to anyone.

What does Technical and Schema check?

Product schema, review markup, canonical tags, index status, llms.txt, and whether your specifications are stated in a form a parser can extract into a field.

What is llms.txt?
Do you check schema on every product?
Does Searchpanel fix the problems for me?
Is this the same as an SEO site audit?
Do you check the words on my listing too?
How often is it re-checked?

Your competitors are already being recommended

Your competitors are already being recommended

Find out which of your products AI names, which it ignores, and what it would take to change that.

Find out which of your products AI names, which it ignores, and what it would take to change that.

Start free trial

Start free trial

Start free trial

Start free trial

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