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TECHNICAL AI SEARCH OPTIMIZATION

Fix the technical problems that keep your products out of AI search

Pick a product you know should win: in stock, priced right, live on your website. Ask ChatGPT to recommend one like it. When a competitor comes back instead, somewhere between your database and the AI's answer, the technical chain dropped it.

Technical AI search optimization · BeRelevant.ai
catalog  ->  template  ->  public page
page     ->  schema    ->  machine-readable offer
offer    ->  feeds     ->  Google / OpenAI / Microsoft
page     ->  crawler   ->  whatever the server returns
The technical chain behind one product

BeRelevant audits that whole chain, finds where products disappear, and works with your developers until the fixes are live and the corrected product is indexable.

Ready to start? Book a 30-min strategy session.

01 - The chain

The buyer sees one page. Search systems see a chain of decisions.

Follow one product on its way to an AI answer.
01

Catalog data feeds a template

Your catalog data feeds a template that builds the page.
02

Schema declares the data

Schema declares part of that data to machines.
03

Feed rules reshape it

Feed rules reshape it again for Google, OpenAI, and Microsoft.
04

Crawlers request the page

Crawlers request the page and get whatever your server decides to give them.

At every step, the chain has to preserve the same product. Lose the variant ID, price, seller, or landing URL along the way, and the search system receives an offer it cannot compare or trust.

Ordinary site audits check the public page and call it done.

We inspect the source data, the transformation, the public output, and the destination record as one system, because the failure can hide in any of them.

02 - The audit

Find where products disappear between Shopify or Salesforce and AI search

We audit the rules that turn catalog data into public products and listings: how parent products, variants, collections, inventory, prices, images, sellers, and regional versions become pages and feeds.
Outputs compared

The page, the schema, and the feed must tell one story

Then we compare the outputs against each other. One missing field can affect a few products. One faulty mapping can affect the entire catalog. Knowing which one you have changes everything about the fix.

The page and feed should show the same price
Each variant needs a stable ID and a canonical URL that resolves
Crawlers need the product content without running your full application
The schema has to declare the offer shown on screen
one product, three records
page price $148.00
feed price $152.00
variant id regenerated
canonical category page
verdict cannot compare
03 - The work

The technical work behind a complete, usable catalog

A standard SEO checklist stops after the first two areas below. We work through all six.
01 - Access

Crawl and index access

Robots rules, CDN behavior, redirect chains, canonicals, sitemaps, pagination, faceted paths, crawl depth, and orphan pages. We check what AI crawlers request and what your server gives them back, which is often not what your browser shows you.

02 - Output

Rendering and page output

We compare source HTML with the rendered page and find product facts that appear only after JavaScript runs. A template failure here repeats across every page built from it.

03 - Declarations

Product and listing declarations

Product, offer, variant, review, seller, location, and relationship markup, checked against the visible page and the source catalog. Machines should never receive conflicting versions of the same offer.

04 - Feeds

Feed and destination integrity

Google Merchant Center, OpenAI product feeds, Microsoft commerce surfaces, and the systems that generate them. Every ID, price, stock level, and landing URL gets verified against the source.

05 - Relationships

Site relationships

Products that exist but cannot be reached through useful links. We repair the paths among content, categories, products, and related items so machines understand how the inventory fits together.

06 - Releases

Release and regression checks

Findings become changes your engineering team can ship. After release, we retest the templates, feeds, and schema to confirm the correction landed without breaking something else.

04 - The disguise

A product-feed problem is often a website problem in disguise

A retailer sells one trail shoe in four colors and twelve sizes. The source catalog holds all 48 offers.
One product, four versions

Then the systems start disagreeing with each other

AI search now has several weak versions of the same product and no stable way to compare them. It recommends a competitor whose one version is clean.

The website declares one parent product
A feed creates new IDs on each export
Several color pages canonicalize to a generic category
Stock updates reach the website hours before they reach the feed
48 offers in the source catalog
website declares 1 parent product
feed ids new on each export
color pages canonicalized away
stock sync hours apart

We trace which system creates each conflict. Repairing that source rule restores the product across many pages and destinations at once.

05 - The scale

Built for the problems that multiply across large sites

Catalog websites rarely fail one URL at a time. They fail by template, rule, locale, seller, or product type.

We handle:

  • Millions of URLs produced by filters and facets
  • Products present in a database but absent from crawlable paths
  • Variant logic that differs across pages and feeds
  • Conflicting price, stock, seller, or regional data
  • JavaScript applications that give crawlers incomplete content
  • Redirect and canonical rules that erase valid products
  • Merchant feeds that silently reject, merge, or expire offers
  • Internal links that never connect advice to the products it recommends

The goal stays simple: when a suitable product is in stock, AI search finds the correct offer and sends the buyer to a page that works.

06 - The follow-through

We stay until the correction is live

An audit document has no value while the same faulty rule keeps publishing broken products.

Your developers own the code. BeRelevant owns the path from diagnosis to verified release: implementation answers, change review, retesting, and coordination across commerce, SEO, content, and engineering teams.

After launch, we keep checking the catalog for new failures caused by releases, feed changes, and new inventory.

Technical AI search optimization

Find the source rule keeping your products out

Show us the commerce platform, website, and feeds. We will trace where eligible products disappear, conflict, or become impossible to compare, and hand your team the specific rule to fix.

Book a 30-min strategy session