Tabstack

Tessa Kriesel, Dan Fernie-Harper, Stafford Brooke, Travis Beauvais, and Les Orchard walked into a problem everyone in AI has complained about: getting data off websites is still a nightmare. Web scraping is fragile, breaks constantly, and requires running your own browser infrastructure like it's 2005. So they built Tabstack, a web data and automation API that just works. Pass a URL and a schema, get back structured JSON that matches every time. No scraper maintenance, no infrastructure headaches, just reliable data extraction.

Here's why this matters: every AI app needs to read the web, but turning unstructured HTML into usable data is a pain that wastes engineering hours. Tabstack's API handles the browser automation under the hood and returns clean, cited results you can actually trust. They launched on Product Hunt in 2026 with a five-person founding team—that's unusual, usually groups that size means they're solving something that requires diverse expertise. The five co-founders suggest this isn't a weekend project; they're building for something real.

The boring problem of web data extraction isn't flashy, but it's the backbone of every AI system that needs real-time information. Tabstack is going after a market that hasn't been solved yet: turn any URL into clean data with one API call. They're five deep, they're new, and they just need companies willing to bet on a better way to scrape.