See Riveter run on your hardest data problem

Riveter finds and structures data from the web using AI agents. You describe what you need in plain language, and its agents search the live web, navigate to the data, and extract it, returning a finished, structured dataset through a single API. Riveter handles bot detection, proxies, and site changes, so pipelines keep working when layouts shift.

Traditional scrapers are written against a specific page structure, so they break whenever a site changes and need ongoing engineering time to maintain. Riveter uses AI agents that interpret pages the way a person would, so the same configuration keeps working after a redesign. Riveter also runs multi-step workflows: it can find every dental practice in San Francisco, then pull every dentist from each one, in a single request. Riveter extracts from complex sites without the cost and latency of full browser rendering.

Riveter fetches data from live websites at the moment you ask, rather than serving results from a pre-built index. This matters for pricing, inventory, job postings, funding announcements, and anything else that changes daily or hourly, where a cached index can be weeks out of date. For particularly frequent updates, like sports scores or election results, you can get fresh data as often as every minute.

Riveter extracts from more than web pages. It reads PDFs and images, calls third-party APIs as part of a workflow, and combines those results with data pulled from the web in a single output. Teams use this to pull figures out of published reports, read specifications from product images, and enrich company records with contact details. Everything returns in the same structured format regardless of where it came from.