Price intelligence for e-commerce — automated competitor & supplier tracking

About 2 million mid-market e-commerce and resale businesses today lack scalable, accurate ways to track competitor prices and supplier availability across Amazon, Walmart, marketplaces and direct channels, and that gap costs sellers margin and sourcing agility in environments where price bands of a few percent matter. The problem is operational and technical: teams spend weeks aggregating noisy listings, mis-match SKUs, and miss short-lived supplier opportunities or repricing windows. You could build a SaaS price-intelligence platform that automates cross-channel crawling and API ingestion, applies embedding-based product matching and vision models to reduce false positives, and surfaces SKU-level price history, stock alerts, supplier lead-time changes and automated repricing or sourcing recommendations. Target commercial packaging around an average $4,500 ACV with tiered plans for single-channel sellers up to enterprise integrations with ERPs and repricers. This is an attractive moment: the total addressable market is roughly $9.0B (2M businesses x $4.5K ACV), the market score for the idea sits at 92/100 and revenue potential at 88/100, and three structural trends—marketplace dominance, AI product matching maturity, and persistent retail margin pressure—converge to increase willingness to pay. Adoption risks are manageable because recent progress in embeddings and vision models materially reduces SKU matching errors that previously prevented automation at scale. You can stand out by investing in match accuracy, explainable signals, rapid onboarding, a supplier data layer and tight integrations so customers see ROI within 30–90 days, but be honest about the challenges: continuous anti-scraping defenses, platform terms of service, the cost of building high-quality training labels and the sales effort needed to convince margin-sensitive customers to switch.