Problem

Riveter addresses the gap between information that exists on the web and data that teams can actually use. Traditional web-data pipelines take months to build, require custom work for each new use case, and degrade as websites change; the alternatives are manual research, list building, fragile scrapers, and stale data. Teams may spend hours or hire contractors to copy product specifications, check competitor pricing, perform KYC/KYB research, or scrape filings, while engineering teams lose time maintaining one-off pipelines.

The central use case is turning a natural-language request into a live, custom dataset and then enriching every row with information gathered from the web. That is particularly valuable for sales-list creation, product and competitor research, pricing intelligence, and other workflows where the required data is too specific or dynamic to buy from a standard database.

Product / Service

Riveter is an AI-powered web data extraction and enrichment platform delivered through a web interface and a single API. A user can upload a dataset or ask Riveter to build one, describe the desired output in a prompt, and have its agents search the web, navigate sites, read pages, PDFs, and images, and return structured results with sources and reasoning for each cell. The API supports both dataset creation and row-level enrichment, so an existing enrichment can be reused with new input data in production.

The product is positioned as a complete system rather than a scraper: it manages changing sites and bot detection, supports websites, PDFs, and third-party APIs, and can enrich one record or millions. Results can be retrieved through the API or webhooks, while monitors can run enrichments on a schedule to keep information current. The benefit is faster, more auditable research without requiring users to build and maintain scraper infrastructure or write custom integrations for every new question.

Market

Riveter competes in AI-powered web data extraction, data enrichment, web-research automation, and market-intelligence software, with especially clear relevance to B2B, e-commerce, sales/GTM, and market research. Its adjacent competitors include Firecrawl, which offers an API to search, scrape, and interact with the web at scale; Bright Data, which offers a web-scraping API and ready-made scrapers; and Apify, which provides tools for real-time web data, competitor tracking, and lead generation. Riveter’s differentiation is the combination of agentic browsing, prompt-driven dataset construction, enrichment, evidence, and scheduled monitoring in one API-first workflow.

Riveter is an active YC Fall 2024 company rather than a pre-product project. Its YC materials report more than 2 million automated searches, usage by Snap and Pietra, and customer use cases at Sunco; another YC profile names Snapchat and OpenAI among customers. The company is YC-backed and a company record reports a $500,000 pre-seed round from Y Combinator. The evidence demonstrates meaningful customer traction, but it does not establish a company-reported revenue figure; a third-party profile estimates $1.2 million of 2025 revenue, so Riveter is best described as commercially active with revenue not independently confirmed in the available sources.