Stop scraping: AI product-matching + resilient price-change monitoring

Retailers and brands increasingly need reliable, real-time competitor and channel pricing signals, but traditional web scraping and brittle SKU-matching break down at scale because of anti-bot defenses, multilingual catalogs, and noisy identifiers. This problem touches roughly 1,000,000 retailers and supports an addressable market of about $6.0B (assuming a $6K ACV). You could build a managed data service that replaces fragile scrapers with compliant collection channels (APIs, commercial partnerships, controlled headless capture) combined with ML-powered entity matching using embeddings and LLMs to link SKUs across stores and languages, plus a resilient price-change alerting layer with clear SLAs for freshness and latency. Timing is favorable: dynamic pricing adoption, improvements in ML-for-entity-matching, and escalating anti-scraping defenses drive demand for managed solutions; the opportunity is reflected in a Market Score of 94/100 and Revenue Potential of 88/100, with competition assessed as medium. This product can differentiate by proving concrete metrics — for example targeting 90–95% match F1 and demonstrable sub-hour (ideally sub-15-minute for key segments) price-change detection — and by combining technical accuracy with legal/commercial data access that reduces buyer risk. The honest challenges are operational cost to maintain coverage, the engineering effort to lower false positives, and negotiating sustainable data partnerships; pursue this if you can field expertise in entity resolution, ops to maintain compliant access, and a go-to-market that reaches mid-market retailers willing to pay ~$6K ACV, otherwise treat it as a tough, capital- and relationships-intensive play.