mudpie

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.

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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.

About the author

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.

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