Company profile · 3 min read
Riveter: live web data as structured APIs
Riveter searches, scrapes and structures live web data through APIs, datasets, webhooks and MCP for research and enrichment workflows.
Published · Updated
What it does
Riveter turns the web into structured data. Its API and agents search, browse, extract and enrich live web information, including pages, PDFs and images, then return typed datasets or reusable data APIs. The YC profile says customers such as Snapchat and OpenAI use Riveter to enrich datasets; the launch describes a prompt-to-list workflow without building and maintaining scrapers manually.
The fit is a data, growth, risk or research team that needs current web evidence at scale. Riveter is more useful than a static data vendor when the target list or field definitions change frequently, but it also puts source quality, terms and repeatability under the microscope.
Why I’d look closer
The developer surface is concrete. Riveter’s docs describe datasets, enrichments, runs, webhooks, row caps, an MCP server and direct API access. The platform can build an endpoint from a natural-language description, return typed JSON and repair an endpoint when a source site changes. That is a strong operational wedge for teams that otherwise run headless browsers or contractors.
The founder context is relevant. The YC biographies describe Abigail Grills with Gusto and Middesk product experience and Cody Watters with engineering/product experience across YC-backed companies. The company claims teams use Riveter for custom KYB/KYC, competitor pricing and product data; those examples are company-described use cases.
What I’d ask
How are source permissions, robots rules, provenance and schema drift handled, and can a user inspect the evidence behind each row? I’d build one small enrichment, compare it to a hand-reviewed sample, and test retries, rate limits, webhook delivery and cost per row before scaling.
My editorial take
Riveter is a strong fit when the web is the changing data source and a static vendor cannot keep up. The API/run model is a real advantage. The buyer should judge it on evidence quality and repeatability, not the speed claim alone.
Quick facts
| Field | Sourced detail |
|---|---|
| Buyer fit | Data, research, growth and risk teams needing live web enrichment |
| Product | Search, scraping, structured extraction, datasets, APIs, webhooks and MCP |
| Developer limits | Public docs show row caps and 30 requests/minute default rate limit |
| Public pricing | Not exposed in the sources checked |
Sources checked
Checked 2026-09-20.
| Source | Used for |
|---|---|
| YC company profile | Product, founders and customer context |
| Riveter homepage | Current product surface |
| Riveter docs | API, runs, webhooks, limits and MCP surface |
Cohort context
Riveter is listed in Fall 2024. In our 2026-09-18 directory snapshot, 57 of 94 listed companies in that cohort have YC’s primary industry label B2B (60.6%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19T16:14:43.373Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.
| Signal | Homepage observation |
|---|---|
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Observed |
| Pricing link | Observed |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |
Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.
