Make fashion catalogs readable to AI shopping agents

Get your products recommended
by AI shopping agents

AI shopping agents can't understand inconsistent fashion catalogs. One brand may label black as Jet, another Noir, and another Blk. Masira standardizes them all as "Black," so AI recommends your products instead of your competitors'.

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Read by the agents your customers already use

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Attribute normalization

Three brands describe black three different ways.

Masira standardizes them all as "Black," so AI shopping agents understand and recommend your products to shoppers

JetNoirBLK

PATCH /v1/products/normalize

{

"color": "Black",canonical

"aliases": ["Jet", "Noir", "BLK"],

"confidence": 0.97

}

How it works

From fashion catalog to AI-ready product data

stage: "import"

Import the catalog you already have

CSV files, ERP systems, or spreadsheets. Masira transforms your existing fashion catalog into AI-ready product data.

POST /v1/catalog/import

blk wool coat, warm, midi

rose silk slip dress, light

ecru linen suit, tailored, summer

sage oversized cashmere jumper

CSVJSONXMLXLSX
stage: "normalize"

Every variation becomes one standard value.

Jet, Noir, and BLK all become Black, so AI shopping agents understand your catalog

blk →

"color": "black"

confidence 0.97

wool →

"fabric": "wool"

confidence 0.97

midi →

"length": "midi"

confidence 0.97

warm →

"warmth": "high"

confidence 0.97

stage: "enrich"

Add the details AI needs

Masira automatically adds missing product details like care instructions, season, and warmth, with a confidence score for every value.

— →

inferred

"care": "dry-clean"

confidence 0.62

— →

inferred

"season": "winter"

confidence 0.60

stage: "publish"

Ready for every AI shopping agent

Publish one AI-ready catalog that shopping agents can search, understand, and recommend

GET /v1/products?color=black&fabric=wool

{

"name": "Véga Cocoon Coat",

"brand": "Maison Véga",

"price": 340,

"color": "black",

"fabric": "wool",

"length": "midi",

"warmth": "high",

"care": "dry-clean",

"season": "winter",

"in_stock": true

}

// Find me a black wool coat under $400

Véga Cocoon Coat by Maison Véga, $340, in stock in your size.

Why AI ready catalogs get recommended

Shoppers are asking AI what to buy instead of browsing websites. AI shopping agents rely on structured product data, not product pages. Masira makes your fashion catalog AI readable, so AI shopping agents can accurately find, rank, and recommend your products.

The platform

Everything your catalog needs to be recommended by AI

Masira takes your existing catalog, standardizes it, fills in missing information, removes duplicates, and publishes it in a format AI shopping agents can understand

Normalize every product attribute

Different brands describe the same thing in different ways. Masira standardizes every variation without changing your original data

colour: Jet→"color": "black"

100% Merino→"fabric": "wool"

knee-length→"length": "midi"

A rail of tailored garments

Publish everywhere

Publish your catalog in formats AI shopping agents and connected platforms can understand

GET /feeds/acp.json

GET /feeds/mcp.json

GET /feeds/schema-org.xml

Consistent product matching

When every product uses the same language, AI shopping agents can accurately find, match, and recommend your products

black wool coat, midi
color: blackfabric: woollength: midi

Complete missing product data

Masira automatically adds missing product details with a confidence score for every AI-generated value

warmth: high · 0.7care: dry-clean · 0.62season: winter · 0.6

One product. One trusted record

Merge duplicate product listings from multiple catalogs into one trusted product record

1 ×Véga Cocoon Coat

Why Masira

Built for the way AI shops.

Fashion First
Built specifically for fashion catalogs.
Original Data
Your original catalog is never changed.
Works Anywhere
Connect your existing catalog in minutes.
AI Ready
Built for AI shopping agents.

The future of AI shopping

AI shopping agents won't browse websites the way people do. They'll rely on structured product data to search, compare, and recommend products. Masira is building the infrastructure that makes fashion catalogs readable to AI.

FAQ

Questions, answered plainly.

What is Masira?

Masira is a catalog intelligence platform that makes fashion catalogs AI-readable. It unifies messy cross-brand product data into one clean, governed schema so AI shopping agents can read, rank, and recommend the right products.

What is agentic commerce?

Agentic commerce is shopping done by AI agents on a customer's behalf: the agent searches, compares, and buys instead of a human browsing product pages. Agents don't see photography or brand storytelling. They read structured data, so a catalog that isn't machine-readable is invisible to them.

Why does my catalog need to be AI-readable?

More shoppers are using AI to discover products instead of browsing websites. AI shopping agents rely on structured product data to search, compare, and recommend products. If your catalog isn't AI-readable, your products are harder for AI to understand and recommend.

How does Masira make a catalog AI-readable?

Masira ingests your existing product feed, normalizes every attribute against a governed fashion taxonomy (so “Jet”, “noir”, and “blk” all resolve to “black”), enriches and deduplicates entries, then publishes one clean structured feed agents can query. Source values are kept. Nothing is overwritten.

Which protocols and formats does Masira support?

Masira publishes schema.org-aligned structured product data and speaks emerging agent protocols, including agentic commerce protocols (ACP) and MCP-compatible endpoints, alongside standard feed exports.

How do I get started?

Connect a product feed from the Masira dashboard and get started free. The first normalized preview takes minutes. Questions first? Reach the team through the contact page.

Make every product AI-readable

Connect your existing catalog and let AI shopping agents find, compare, and recommend your products

Get started free