Structured data for 50K+ products across 20 retailers. Cross-retailer matching, clean specs, and a knowledge layer — so you build features, not data pipelines.
Cross-retailer pricing, already matched. Build comparison features without building matching infrastructure.
Give your agent structured product tools via MCP or REST. Specs, pricing, and knowledge — not web scraping.
Maintenance schedules, lifespan data, and replacement planning for every product type — structured as API data.
Error codes, failure modes, and fix steps — structured as API data, not buried in PDFs and forum posts.
Product data infrastructure takes months. Scraping, matching, normalizing, and maintaining it takes a team. With Covala, that's one API key.
Parsers for 20 retailer websites, each with different HTML, anti-bot measures, and format changes.
Matching the same product across stores where names, model numbers, and formatting differ.
Converting inconsistent retailer specs into typed, filterable fields you can actually compare.
Organizing 620+ product types into a consistent hierarchy with categories, types, and brands.
Maintenance schedules, error codes, buying guides, lifespan data, and cost of ownership — per product type.
Grouping the same product in different colors, sizes, and configurations into a single entity.
A product knowledge API provides structured, normalized data about physical products — specifications, pricing, brand information, and knowledge like maintenance schedules and error codes. Instead of scraping retailer websites and building your own data pipeline, you query one API and get clean, typed JSON for 50K+ products across 20 retailers.
Covala matches the same product across different retailers using GTIN barcodes and manufacturer part numbers (MPN). A Samsung dishwasher listed as three different names on three different websites gets resolved to a single product identity with offers from each retailer.
The Covala MCP server lets AI agents call product search, lookup, and knowledge tools directly through the Model Context Protocol. Add it to any MCP-compatible agent (Claude, GPT, custom agents) with one config line — no custom integration code needed.
Request format=ai to get a condensed ~300-token summary per product instead of the full ~2,000-token JSON response. This fits more products into an agent's context window while preserving the key specs, pricing, and knowledge data.
Covala covers 620+ product types across appliances, electronics, computing, and home equipment — including dishwashers, refrigerators, TVs, laptops, speakers, and more. Each product type has structured specifications, cross-retailer pricing, and a knowledge layer with maintenance schedules, error codes, buying guides, and lifespan data.
The Covala catalog grows daily — the goal is 1,000+ new products added every day, along with new retailers and product types. Knowledge data, brand profiles, and product type coverage expand continuously. Your integration improves automatically as the dataset grows, with no code changes needed on your end.
50K+ products, 20 retailers, 620+ product types, and a knowledge layer — all from one API.