Building web data pipelines typically requires weeks of custom development, fragile scrapers, and ongoing maintenance, leading to stale or incomplete data and high engineering overhead.

Riveter provides a fully automated web data extraction platform that eliminates the need for hand‑crafted scrapers. Users submit natural‑language prompts, and Riveter’s AI‑driven agents search the web, navigate pages, and extract structured data such as company lists, contact profiles, or change‑monitoring signals. The service delivers results via a credit‑based API, handling SERP queries, page scraping, image and PDF extraction, and proxy management without user‑level code. By abstracting the entire pipeline—from discovery to enrichment—Riveter enables teams to build and scale datasets quickly, with built‑in data freshness and reliability. The platform integrates with existing workflows through API endpoints and supports custom integrations for enterprise customers.

Prompt‑driven agent searches that autonomously browse, locate, and extract target information from the open web

Unified API supporting SERP, HTML scraping, image OCR, PDF parsing, and proxy rotation

Credit‑based pricing per action (e.g., 1 credit per search, 0.05 per page scrape, 2 credits per contact lookup)

Built‑in change‑monitoring to detect updates to previously collected records

Scalable list building with support for 50‑to‑10,000‑row datasets and enrichment per row

Custom integration and white‑glove onboarding options for enterprise deployments

Primary customers are growth, marketing, and data‑engineering teams that need large‑scale, up‑to‑date web‑derived datasets for lead generation, market research, and competitive intelligence.

Revenue is generated through subscription plans (Free, Self‑Serve, Enterprise) that allocate monthly credit bundles, as well as a pay‑as‑you‑go model where users purchase credits in advance and consume them per action.