Competitor Intelligence Engine

Most brands can tell you their own catalog in exhaustive detail: pricing, stock, promotions, down to the SKU. Ask what a specific competitor did in the same categories this week, and the answer is usually a guess from a manual spot-check, done too rarely to catch anything before it costs margin or shelf share.

The gap isn't a lack of data; competitor pricing and assortment sit in plain sight on public storefronts. The gap is that nobody's watching continuously. So I built a monitoring pipeline that tracks competitor pricing, assortment, and positioning across marketplaces on a recurring schedule, turning that comparison into a dashboard a brand checks rather than a project someone has to remember to run.

A pricing dashboard nobody trusts is worse than no dashboard at all.

Most of the engineering effort didn't go into collecting prices; it went into deciding when not to trust one. A page that fails to load and gets read as "price dropped to ₹0" is worse than a missing data point, because it's confidently wrong. So the pipeline spends more logic distinguishing a real price change from a broken page, a temporary stockout, or a currency formatting glitch than it does on the scrape itself.

The exact detection logic and results stay private, but the lesson travels: in any monitoring system, the unglamorous work of not lying to your users about data you're unsure of matters more than the flashy part of collecting it.