For shopping & comparison apps

Cross-retailer data, already matched

Same dishwasher, three retailers, three names, three formats. Covala resolves it to one product with structured offers from each store — so you build comparison features, not matching infrastructure.

Entity resolution

Same product across 20 retailers, matched by GTIN and MPN. One product identity, multiple offers.

Normalized specs

Inconsistent retailer specs converted to typed, filterable fields. Compare dimensions, capacity, and features across stores.

Real-time pricing

Current prices from 20 retailers attached to each product. No scraping, no rate limiting, no maintenance.

One API call

Search, match, compare — already done

GET /v2/products/search?q=samsung+dishwasher&include=offers
{
  "data": [
    {
      "id": "prod_8f3k2m",
      "name": "Samsung Smart 42dBA Dishwasher with StormWash+",
      "brand": "Samsung",
      "gtin": "887276726014",
      "specs": {
        "noise_level": "42 dBA",
        "capacity": "15 place settings",
        "width": "23.875 in",
        "energy_star": true
      },
      "offers": [
        {
          "retailer": "Best Buy",
          "price": 849.99,
          "url": "https://bestbuy.com/..."
        },
        {
          "retailer": "Home Depot",
          "price": 898.00,
          "url": "https://homedepot.com/..."
        },
        {
          "retailer": "Lowe's",
          "price": 849.99,
          "url": "https://lowes.com/..."
        }
      ]
    }
  ],
  "meta": { "total": 23, "cursor": "eyJpZCI6..." }
}

format=ai returns ~300 tokens instead of ~2,000

What you don't have to build

A comparison app needs data from every retailer, matched and normalized. That's months of infrastructure you can skip.

Scraping 20 retailer websites

Anti-bot protections, HTML format changes, rate limiting, and downtime monitoring for every retailer you want to cover.

Product matching engine

Name variations, model number formats, and missing identifiers make cross-retailer matching a full-time engineering problem.

Price monitoring infrastructure

Polling schedules, rate limit management, data freshness tracking, and alerting when a retailer changes their page structure.

Spec normalization pipeline

One retailer says '23.875 inches', another says '60.6 cm', a third says '24"'. Converting to consistent, typed fields takes real work.

Variant grouping logic

Same dishwasher in stainless steel, black stainless, and white — three SKUs that should be one product with finish options.

Beyond the basics

More than price tags

Price comparison gets users in the door. These features keep them coming back.

Knowledge layer

Maintenance schedules, buying guides, and cost of ownership data for every product type — surface insights your competitors can't.

Brand intelligence

Quality tiers and cross-category coverage for 1,400+ brands. Help users understand what they're buying, not just the price.

AI-optimized responses

Token-efficient format designed for LLM context windows. Build AI shopping assistants that compare products in conversation.

Shopify taxonomy

620+ product types in an industry-standard hierarchy. Filter, categorize, and navigate products with a consistent structure.

Frequently asked questions

What is cross-retailer entity resolution?

Cross-retailer entity resolution is the process of identifying the same product across different stores. Retailers use different names, model numbers, and formatting — so a Samsung dishwasher might appear as three unrelated products. Covala resolves this by matching products using GTIN barcodes and manufacturer part numbers (MPN), collapsing duplicates into a single product identity with offers from each retailer.

How do I compare product prices across retailers?

With the Covala API, add include=offers to any product query. Each product in the response includes an offers array with current prices, retailer names, and direct URLs for every store that carries it. You get structured price comparison data without scraping or maintaining retailer integrations.

Can I build a comparison app without scraping?

Yes. Covala provides pre-matched products from 20 retailers with structured specs, pricing, and metadata. You query one API and get cross-retailer data that would otherwise require building and maintaining scrapers for every retailer, plus a matching engine to connect them.

What product data does the Covala API return?

Each product includes structured specifications (dimensions, capacity, features), pricing from up to 20 retailers, brand information, images, category and product type, GTIN and MPN identifiers, and a knowledge layer with maintenance schedules, buying guides, and cost of ownership data. Request format=ai for a condensed ~300-token summary optimized for LLM context windows.

How does Covala handle different product names across retailers?

Covala uses entity resolution to match products across retailers. When Best Buy lists a product as 'Samsung DW80B7070US' and Home Depot lists it as 'Samsung Smart 42dBA Dishwasher', Covala matches them by their shared GTIN barcode or MPN. The result is one canonical product with a clean name and offers from both stores.

Build comparison features, not data pipelines

50K+ products, 20 retailers, structured specs, and cross-retailer pricing — all from one API key.