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SCHEMA & METADATA

All of your markup, generated from your own data.

All of your markup, generated from your own data.

Searchpanel writes the corrected JSON-LD, meta tags, and llms.txt entries for every product, filling each field from your own specifications rather than guessing, then re-runs the checks that flagged the problem before anything ships.

Searchpanel writes the corrected JSON-LD, meta tags, and llms.txt entries for every product, filling each field from your own specifications rather than guessing, then re-runs the checks that flagged the problem before anything ships.

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 GETS GENERATED ]

Everything a machine needs, written for you.

Everything a machine needs, written for you.

Product schema, review markup, meta and social tags, llms.txt entries, and the validation pass that has to clear before any of it ships.

Product schema, review markup, meta and social tags, llms.txt entries, and the validation pass that has to clear before any of it ships.

Product schema

A complete Product block with the fields engines need to match a SKU to a real item, each value pulled from your catalogue.

Review markup

Ratings written as numbers in valid aggregateRating markup, so an engine reads a score instead of a sentence.

Meta and social tags

The title tag, meta description, and social cards, written from the same facts as the listing rather than duplicating it.

llms.txt entries

A machine-readable entry per product in your llms.txt file, so AI tools that read it see your full catalog.

Validated before it ships

Every generated block is re-run against the same checks that flagged the problem, and a block that fails cannot be approved.

See the markup your products are missing.

Run a free audit and see which fields are empty or broken on your best sellers, and what the corrected block would contain.

Audit your products free

Audit your products free

Product schema

A complete Product block with the fields engines need to match a SKU to a real item, each value pulled from your catalogue.

Meta and social tags

The title tag, meta description, and social cards, written from the same facts as the listing rather than duplicating it.

Validated before it ships

Every generated block is re-run against the same checks that flagged the problem, and a block that fails cannot be approved.

Review markup

Ratings written as numbers in valid aggregateRating markup, so an engine reads a score instead of a sentence.

llms.txt entries

A machine-readable entry per product in your llms.txt file, so AI tools that read it see your full catalog.

See the markup your products are missing.

Run a free audit and see which fields are empty or broken on your best sellers, and what the corrected block would contain.

Audit your products free

Audit your products free

[ HOW A BLOCK IS WRITTEN ]

Tools to help you fill the fields and prove they pass.

Tools to help you fill the fields and prove they pass.

Every value is drawn from your own catalogue, generated as the markup each surface expects, and re-run against the same checks that flagged it in the first place.

Every value is drawn from your own catalogue, generated as the markup each surface expects, and re-run against the same checks that flagged it in the first place.

THE FILL

See every empty field filled from your own catalogue.

A value is taken from a specification sheet, a lab result, or a connected marketplace listing. If it exists nowhere, it is reported as missing rather than filled with a plausible guess.

  • Each value traced to its source
  • Nothing invented to complete a block
  • Genuinely missing values stay flagged

THE OUTPUTS

See every surface written from the same facts.

The JSON-LD, the meta tags, and the llms.txt entry are generated together from one set of values, so a product cannot describe itself three different ways across three places.

  • One source, three outputs
  • No drift between surfaces
  • Regenerated together on any change

THE RECHECK

See it pass the check that flagged it.

Generated markup is re-run against the same validation that raised the problem, and because most faults come from one template, a single approved block clears every product built on it.

  • Re-run on the same checks
  • Applied across the template
  • A failing block cannot be approved

2,847

buyer prompts tracked per brand, scored every day

5×

average lift in AI visibility within the first 90 days.

+32%

higher conversion on AI referral traffic.

2,847

buyer prompts tracked per brand, scored every day

5×

average lift in AI visibility within the first 90 days.

+32%

higher conversion on AI referral traffic.

2,847

buyer prompts tracked per brand, scored every day

5×

average lift in AI visibility within the first 90 days.

+32%

higher conversion on AI referral traffic.

How Herbs & Kettles strengthened discovery against larger tea brands.

Herbs & Kettles had a strong farm-to-cup story, but that differentiation wasn’t translating into enough digital visibility. Searchpanel revealed where the brand was missing from AI-generated recommendations, which competitors were being surfaced instead, and which product signals were limiting discovery.

Using those insights, Herbs & Kettles refined product pages, metadata, and structured data around high-intent themes like sugar-free matcha and loose-leaf chai. Within a quarter, key products gained stronger search visibility while ad-attributed sales more than doubled on the same total spend.

Dr. Poorvi Chordia

Co-Founder · Herbs & Kettles

Ad driven revenue

+214.7%

on flat ad spend

TACoS efficiency

+36%

▲ 103.3% ROAS

Sales growth

+114.3%

quarter on quarter

How Herbs & Kettles strengthened discovery against larger tea brands.

Herbs & Kettles had a strong farm-to-cup story, but that differentiation wasn’t translating into enough digital visibility. Searchpanel revealed where the brand was missing from AI-generated recommendations, which competitors were being surfaced instead, and which product signals were limiting discovery.

Using those insights, Herbs & Kettles refined product pages, metadata, and structured data around high-intent themes like sugar-free matcha and loose-leaf chai. Within a quarter, key products gained stronger search visibility while ad-attributed sales more than doubled on the same total spend.

Dr. Poorvi Chordia

Co-Founder · Herbs & Kettles

Ad driven revenue

+214.7%

on flat ad spend

TACoS efficiency

+36%

▲ 103.3% ROAS

Sales growth

+114.3%

quarter on quarter

How Herbs & Kettles strengthened discovery against larger tea brands.

Herbs & Kettles had a strong farm-to-cup story, but that differentiation wasn’t translating into enough digital visibility. Searchpanel revealed where the brand was missing from AI-generated recommendations, which competitors were being surfaced instead, and which product signals were limiting discovery.

Using those insights, Herbs & Kettles refined product pages, metadata, and structured data around high-intent themes like sugar-free matcha and loose-leaf chai. Within a quarter, key products gained stronger search visibility while ad-attributed sales more than doubled on the same total spend.

Dr. Poorvi Chordia

Co-Founder · Herbs & Kettles

Ad driven revenue

+214.7%

on flat ad spend

TACoS efficiency

+36%

▲ 103.3% ROAS

Sales growth

+114.3%

quarter on quarter

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 Schema and Metadata generate?

Product JSON-LD, review markup, meta and social tags, and llms.txt entries. Finding what is broken is Technical and Schema; this writes the correction.

Where do the field values come from?
What if a required value exists nowhere?
Is the output validated?
Can it run across the whole catalogue?
Do I have to deploy it myself?
How is this different from a schema generator?

Turn AI visibility into revenue.

Turn AI visibility into revenue.

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.

Run a free audit

Run a free audit

Run a free audit

Run a free audit

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