The AI commerce readiness layer

Make your product catalog ready for AI shopping agents.

FeedLayer turns fragmented product data into structured, trustworthy, AI-ready feeds and product intelligence — so your products can be discovered, compared, recommended, and purchased by the next generation of shopping agents.

Join the waitlist See how it works

OpenAI-compatible feed output Variant + policy normalization Product intelligence graph

feedlayer.app / pipeline

live

Messy inputs·

Supplier pages

Spreadsheets

Images

PDFs

Marketplace listings

Inventory systems

Support records

Structured product context·

product.schema.jsonvalidated

{
  "id": "sku_8431",
  "title": "Aurora Linen Shirt",
  "brand": "Northwind",
  "variants": [
    { "color": "indigo", "size": "M", "stock": 42 },
    { "color": "ivory", "size": "L", "stock": 17 }
  ],
  "attributes": {
    "material": "100% European linen",
    "season": "SS25",
    "care": "machine wash cold"
  },
  "buyer_intent": ["breathable", "office", "warm-weather"],
  "policies": { "returns": "30d", "shipping": "free >$60" },
  "trust": { "reviews": 1284, "rating": 4.7 }
}

Coverage

98%

Confidence

0.94

Variants

AI-ready outputs·

01AI-ready product feeds

02Product intelligence graph

03Variant normalization

04Buyer intent mapping

05Availability + policy context

Shopping agentsstreaming

GPTClaudePerpGemini

The problem

Product data was built for marketplaces. Agentic commerce needs more.

Existing product data is scattered and optimized for human shoppers, SEO, and marketplace search. AI shopping agents need richer, structured context to understand, compare, recommend, and purchase products correctly.

01 / 03

Fragmented sources

Product data lives across supplier pages, spreadsheets, PDFs, images, marketplace listings, inventory systems, and support records.

supplier.htmlskus.xlsxspec.pdfimg_42.pngamzn.json

02 / 03

Agent-unready catalogs

Traditional catalog tools optimize for SEO and platform listings, not AI agents that reason across variants, policies, trust signals, and buyer intent.

SEO keywords✓ optimized

Buyer intent✕ missing

Policy context✕ missing

03 / 03

Invisible products

Incomplete or messy data can cause products to be ignored, misunderstood, or incorrectly recommended by AI shopping agents.

Agent query: "best linen shirt under $80"

no_match · low_confidence_0.21

Why now

AI is becoming the next shopping interface.

Search commerce was built around SEO and ad feeds. Marketplace commerce around platform listings. Social commerce around creators. Agentic commerce will be built around machine-readable product context.

01Then
Search commerce
SEO and ad feeds.
02
Marketplace commerce
Amazon, Shopify, TikTok Shop listings.
03Now
Social commerce
Creators and recommendation feeds.
04Next
Agentic commerce
AI agents that discover, compare, recommend, and purchase products.
FeedLayer lives here

The solution

The AI commerce readiness layer.

FeedLayer transforms messy product data into structured, validated, and agent-ready product intelligence.

Ingest

Connect supplier pages, spreadsheets, images, PDFs, marketplace listings, inventory systems, and support records.

Extract

Pull product titles, descriptions, specifications, variants, prices, availability, images, and policies.

Normalize

Standardize attributes, categories, units, currencies, variants, and inventory status.

Enrich

Add buyer intent, comparison-ready attributes, FAQs, image alt text, trust signals, and policy context.

Export

Generate AI-ready product feeds, OpenAI-compatible feed outputs, and product intelligence graphs.

Product modules

Everything your catalog needs for AI shopping.

A composable set of modules that turn raw catalogs into agent-ready commerce intelligence.

POST /v1/feeds.generate
{
  "source": "supplier.csv",
  "format": "openai.product.v1",
  "include": ["variants", "intent", "policies"]
}
→ 1,284 products · 4.2s

AI-ready feed generator

Convert messy product records into structured feeds designed for AI shopping agents.

title✓ pass

price.currency⚠ warn

images[2].url✕ fail

variant.size⚠ warn

policy.returns✓ pass

Feed validator

Detect missing fields, malformed prices, broken image links, weak variant data, and incomplete seller policies.

Indigoindigo blueINDIGO/01

SMLMediumMed.

12 variant dims · normalized → 5 canonical

Variant normalization

Clean up colors, sizes, materials, capacities, bundles, compatibility, and other variant dimensions.

warm-weather travel92

office-appropriate78

gift for partner54

Buyer intent enrichment

Map products to use cases, customer needs, comparison factors, and purchase intent.

Shipping

Free > $60

Returns

30 days

Warranty

1 year

Region

EU + US

Trust and policy context

Structure shipping, return, FAQ, warranty, and seller policy information for agentic commerce.

Product intelligence graph

Connect products, variants, attributes, buyer intents, policies, reviews, and competitors into a usable commerce graph.

Product intelligence

From product records to product intelligence.

AI shopping agents need more than flat catalog rows. They need relationships among products, variants, attributes, use cases, buyer intent, reviews, policies, availability, and competitors.

FeedLayer helps AI agents understand not only what a product is, but who it is for, why it matters, how it compares, and whether it can be trusted.

ProductVariantsAttributesBuyer IntentAvailabilityPoliciesReviewsCompetitors

product_intelligence.graph8 nodes · 7 edges

Differentiation

Not just catalog enrichment. Built for agentic commerce.

Traditional catalog tools

legacy

Optimize for human shoppers
Focus on SEO and marketplace listings
Produce product descriptions and attributes
Manage product records
Help products appear in traditional search and marketplace results

FeedLayer

agent-native

Optimizes for AI shopping agents
Builds structured, trustworthy product context
Creates AI-ready feeds and product intelligence graphs
Adds buyer intent, policy context, and comparison-ready information
Helps products become discoverable, comparable, recommendable, and purchasable by agents

Use cases

Built for modern commerce teams.

Global

Cross-border sellers

Turn supplier data, images, PDFs, and spreadsheets into English AI-ready product feeds.

Brand

DTC brands

Prepare product catalogs for AI shopping discovery, recommendation, and comparison.

Multi-channel

Marketplace sellers

Normalize variants, attributes, availability, and policy data across platforms.

Ops

Product operations teams

Reduce manual catalog cleanup and improve product data consistency across channels.

Early access · MVP

Start with an AI commerce readiness audit.

Upload or connect your product data and get an instant readiness score, missing field report, OpenAI-compatible feed preview, variant cleanup, buyer intent enrichment, and trust signal analysis.

audit / northwind-apparel

1,284 SKUs

AI readiness score

/ 100

Above benchmark for DTC apparel

Missing variant fields

across 9 SKUs

Broken media links

images + 1 video

Policy context

Incomplete

returns + warranty

Buyer intent coverage

Medium

62% mapped

Comparison readiness

Strong

9/10 attrs

OpenAI-compatible feed previewfeed.v1.json

{
  "id": "sku_8431",
  "title": "Aurora Linen Shirt",
  "variants_count": 6,
  "intent": ["warm-weather", "office", "travel"],
  "policies": { "returns": "30d", "shipping": "free>$60" },
  "trust": { "rating": 4.7, "reviews": 1284 },
  "ready_for_agents": true
}

Request early access

Run a free audit on your catalog and see exactly what AI shopping agents will and won't understand about your products.

AI readiness score with benchmark
Missing field + broken media report
OpenAI-compatible product feed preview
Variant + attribute cleanup suggestions
Buyer intent enrichment sample
Policy and trust signal analysis

Prepare your catalog for the next commerce channel.

Agentic commerce is coming. Make sure your products can be understood, trusted, and recommended by AI shopping agents.

Join the waitlist Contact us