A consistent product identity.
Reconcile listings and variants across sources, so downstream systems can work with a coherent product record.
THE DATA LAYER FOR AI COMMERCE
Commerce moves fast.
Give AI the full picture.
From fragmented product data to AI-ready intelligence.
One data foundation for models and shopping agents.
REAL-WORLD COMMERCE. MACHINE-READY CONTEXT.Scroll to explore ↓
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BUILT FOR THE NEXT ERA OF COMMERCE
THE WORLD BEHIND EVERY CATALOG01 / COMMERCE
PRODUCTS. VARIANTS. POSSIBILITIES.REAL-WORLD COMPLEXITY↓ MACHINE-READY CLARITY
Better commerce AI
starts with better data.
Product catalogs live in silos. Attributes don’t match. Prices change. AI needs a coherent view of it all.
A single product can appear across merchant feeds with different names, categories, and variants. Bringing those records together is the first step toward useful commerce intelligence.
Entrosift connects, structures, and delivers commerce data—giving models the context to understand products and shopping agents the information to help people choose.
Attributes AI can understand.
Align categories, units, and product details into structured datasets for training, retrieval, and evaluation.
Context agents can act on.
Make product attributes, offer details, and source context accessible to shopping agents through MCP.
THE ENTROSIFT PLATFORM
From source to intelligence.
Connected at every layer.
One foundation.
Every AI experience.
See how it connects ↘COMMERCE DATA INTEGRATION
Many sources.
One coherent catalog.
Bring product feeds, marketplace data, and merchant catalogs into a shared foundation. Resolve inconsistent formats before they reach your models.
- Catalog and feed integration
- Cross-source product mapping
- Price and availability context
Merchant catalogs ↘Product feeds →Marketplace data ↗
e
UNIFY & NORMALIZE Unified product recordJSON
{
"product_id": "prd_042",
"title": "Everyday Headphones",
"category": "Audio",
"offers": [ … ],
"source": "merchant_feed"
}COMMERCE CONTEXT, END TO END
A direct line from your data
to what AI can do with it.
Connect the sources.
Bring together the product data your business already has.
Make it meaningful.
Normalize taxonomies, reconcile entities, and structure attributes.
Put context to work.
Deliver model-ready datasets and agent-accessible tools.
BUILT FOR SHOPPING AGENTS
Give your agent
a better way
to shop.
A model can understand a request. It needs commerce context to answer it well.
Expose structured product discovery through the Model Context Protocol. Help agents find relevant products, compare attributes, and ground recommendations in source data.
Explore an MCP interaction ↘YOUR COMMERCE DATA. AN AGENT-ACCESSIBLE INTERFACE.
TRY AN ILLUSTRATIVE REQUEST
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Find wireless headphones under $150 for everyday commuting.
MCP TOOL CALL EXAMPLE
products.search({
"query": "wireless headphones",
"max_price": 150,
"currency": "USD"
})STRUCTURED RESPONSE
Everyday Wireless Headphones$129.00
Over-ear · Noise cancellation · 30h battery
↗ Source-linked product context
Example data and tool schema. No live product search.
BUILT AROUND YOUR USE CASE
DATA THAT GOES FURTHEROne data layer.
A world of possibilities.
01 / AI TEAMSTeach models
Teach models
real-world commerce.
Structured product datasets for training, retrieval, and evaluation.
02 / AGENT BUILDERSBuild shopping agents
Build shopping agents
with useful context.
Connect discovery and comparison experiences to commerce data through MCP.
03 / COMMERCE BUSINESSESMake your catalog
Make your catalog
ready for AI discovery.
Turn fragmented merchant data into consistent, machine-readable product context.
OUR APPROACH
The details
make the data.
Useful AI starts with the fundamentals: consistent structure, clear sources, and context that fits the task.
Commerce is the context.
Products, variants, attributes, and offers belong together. Shape the data around how commerce actually works.
Quality before quantity.
Align schemas and taxonomies, identify duplicates, and surface missing fields before downstream use.
Build for both models and agents.
Serve datasets for model workflows and structured interfaces for agent experiences from a common foundation.
LET’S BUILD WHAT COMES NEXT