Infrastructure for AI shopping agents

Turn messy product data into AI-ready feeds.

FeedLayer helps ecommerce sellers clean, normalize, and validate product data so AI shopping agents can understand, compare, and recommend their products.

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Built for ecommerce teams preparing catalogs for AI shopping agents.

Catalog readiness
feed.json
Readiness score
86
/ 100
Apparel benchmark · 64
SKUs scanned1,284
Missing fields9 SKUs18
Variants normalizedof 426412
Variant cleanup
Med.MediumMM
The problem

Product data is not ready for AI shopping.

01

Scattered sources

Product data lives across spreadsheets, supplier pages, PDFs, images, listings, and inventory tools.

02

Messy variants

Colors, sizes, materials, bundles, availability, and categories are often inconsistent across channels.

03

Poor agent understanding

AI shopping agents need structured product context to compare, recommend, and purchase accurately.

Why now

AI is becoming the next shopping interface.

ChatGPT, Perplexity, Gemini, and Google AI Overviews are already routing buyers. The catalog format they read isn't the one Shopify or Amazon was built for.

  1. 01Then

    Search commerce

    SEO and ad feeds.

  2. 02

    Marketplace commerce

    Amazon, Shopify, TikTok Shop.

  3. 03Now

    Social commerce

    Creators and recommendations.

  4. 04Next

    Agentic commerce

    AI agents that discover, compare, and recommend products.

How it works

From messy product data to an AI-ready feed.

A single workflow that turns scattered seller data into a clean feed AI shopping agents can read.

  1. 1

    Ingest

    Pull from spreadsheets, PDFs, supplier URLs, and channel exports.

  2. 2

    Extract

    Identify titles, attributes, variants, prices, stock, and policies.

  3. 3

    Normalize

    Reconcile categories, units, sizes, and availability across sources.

  4. 4

    Validate

    Flag missing or broken fields and score catalog readiness.

  5. 5

    AI-ready feed

    Export a structured feed shopping agents can actually use.

Inside the product

A workspace for catalog teams preparing data for AI.

Dashboards and tools that make catalog readiness measurable, fixable, and shippable.

Readiness score
86
Apparel benchmark · 64
Coverage
Variants
Policies
Missing fields
  • policy.returns
    high
  • image[2].url
    high
  • warranty.duration
    med
  • ship.region.DE
    low
Variant cleanup
Size attribute
Med.MediumMM
412 of 426 variants reconciled
Attribute normalization
material100% linen
fitregular
caremachine wash
seasonspring/summer
Feed preview
  • Aurora Linen Shirt
    AUR-LIN-01 · M / Indigo
    ready
  • Trail Runner 02
    TR-RUN-02 · 9.5 / Sand
    ready
  • Field Tote
    FLD-TT-01 · —
    review
Policy & availability
  • Returns window present
  • Shipping regions defined
  • Warranty terms parsed
  • Stock signal connected
Beyond a flat feed

A product is more than a row in a spreadsheet.

Agents need to know how a product compares, who it's for, what's in stock, and whether it can be returned. We model those relationships once so every agent gets the same answer.

ProductVariantsAttributesBuyer IntentAvailabilityPoliciesReviewsCompetitors
ProductSKUVariantsAttributesBuyer IntentAvailabilityPoliciesReviewsCompetitors
Where we fit

Catalog tools optimize for listings. We optimize for AI.

Traditional catalog tools
FeedLayer
Optimized for
SEO, listings, and marketplaces
AI shopping agents
Output
Channel-specific feeds and ad data
Structured, agent-ready product feeds
Consumer
Search engines and ad platforms
Conversational AI and shopping agents
Who we work with

Built for teams managing real product catalogs.

Ecommerce sellers

Get your catalog ready before AI assistants start sending real traffic.

Cross-border sellers

Convert supplier pages, PDFs, and Excel files into clean, structured feeds.

Product operations teams

Reconcile variants, attributes, and stock across channels in one workflow.

Agencies managing many catalogs

Process thousands of SKUs across client catalogs without rebuilding spreadsheets.

Early access

See FeedLayer with your own catalog.

We're working hands-on with a small group of ecommerce sellers preparing their data for AI shopping agents. Talk to us about a pilot or share a sample of your catalog.

Sample report
Catalog readiness
feed.json
Readiness score
72
/ 100
Apparel benchmark · 64
Missing variant fields
18
across 9 SKUs
Broken media links
4
images + 1 video
What's missing for agents
policy.returnsvariant.size.M_vs_Medimage[2].404intent.warm-weatherwarranty.durationship.region.DE

Talk to the team

Tell us a bit about what you're looking for. We usually reply within 1 business day.

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Prepare your product data for AI shopping.

Talk to us if your catalog is messy, multi-source, or hard to structure.

Contact us