Building a price tracker means solving the hardest problem first: identifying the same product across different retailers. Covala has already done the entity resolution — you just query the API.
Every price tracker has the same bottleneck: before you can compare prices, you need to figure out which listings are actually the same product.
Best Buy calls it “Samsung DW80CG4021SR/AA”, Home Depot calls it “Samsung 24 in. Smart Dishwasher”. How do you know it’s the same product?
Retailer product feeds change format, go stale, and require constant maintenance. One schema change breaks your pipeline. You end up maintaining scrapers, not building features.
A price is just a number without context. Is $849 good for this dishwasher? How does it compare to similar models? Is there a newer version? Raw pricing data can’t answer these questions.
Entity resolution already done. Every product is linked across retailers by GTIN barcode and model number. No matching logic needed on your end — just query by product ID and get all offers.
Current prices from 20 retailers in a consistent format. Same product, all offers, one API call. No need to normalize currencies, parse sale prices, or handle retailer-specific formatting.
Combine pricing with product specs, quality scores, and brand profiles. Help users understand value, not just price. A $549 dishwasher means more when you know it’s 44 dBA with a 10-year lifespan.
Track prices across variants. When the stainless model drops, show the black stainless and white options too. Variant grouping means your users never miss a deal on the product they actually want.
Query a single product and get normalized pricing from every retailer that carries it. No scraping, no feed parsing, no entity matching on your side.
Entity resolution, normalized pricing, and product context — already done for 50K+ products across 20 retailers. Focus on your product, not your data pipeline.
Pre-matched products + normalized pricing + product context