Riveter
Riveter turns a plain-language prompt into a finished, structured web dataset through one AI-agent API — search, scrape, enrich, and monitor in a single
If your team is paying engineers to maintain scrapers or analysts to assemble lists by hand, Riveter is a credible replacement, and per-action credits (1 per agent search, 0.05 per page scrape, 2 per contact lookup, 2–4 per list-building result) make the math legible before you commit. The free plan's 250 credits is enough to test whether the agent actually finds your specific sources. Where it bites is scale economics: building a 2,500-row list on Self-Serve at 2–4 credits per result can burn most of the 10,000 monthly credits in one run. Compare Exa, Parallel, and Firecrawl for raw search-and-scrape primitives, and Clay for no-code enrichment lists.
Verified 2d ago · liveness 72/100 · cite: rightaichoice.com/tools/riveter
- Data and engineering teams replacing in-house scraper maintenance with an API that returns finished datasets
- Sales and GTM teams building custom lead lists enriched with ICP qualifiers and contact data
- Pricing and competitive research analysts tracking competitor pricing, features, and payment providers
- Operations teams scheduling change monitoring across key web sources
- Pipelines that need raw HTML crawl at millions of known URLs, where per-result credit costs outweigh the workflow
- Teams wanting a fully no-code product — the platform is API-first and configuration leans technical
- Compliance or outbound workflows that can't absorb agent-based extraction errors without human spot-checking on a sample
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Skip Riveter if you need to crawl millions of already-known URLs cheaply, or if you want a no-code tool rather than an API-first platform you configure through prompts and code.
List building runs 2–4 credits per result, so a 2,500-row build can consume most or all of Self-Serve's 10,000 monthly credits in a single run
Self-Serve at $249/month for 10,000 credits fits a funded data, GTM, or research team running scheduled pipelines rather than one-off jobs. Below it, $15–$120 one-time credit packs with no expiration suit project work and let you try Riveter without a subscription. Above it, Enterprise is custom credits with 100,000-row API requests and dedicated infrastructure. Against Exa, Parallel, or Firecrawl — which meter search and scrape calls — compare on the cost of your finished rows, not the
In short
Riveter — Riveter turns a plain-language prompt into a finished, structured web dataset through one AI-agent API — search, scrape, enrich, and monitor in a single. Best for Data and engineering teams replacing in-house scraper maintenance with an API that returns finished datasets, Sales and GTM teams building custom lead lists enriched with ICP qualifiers and contact data, Pricing and competitive research analysts tracking competitor pricing, features, and payment providers. Free to start; paid plans from $15.
What's new in Riveter
Checked 3 days agoAcross the latest 10 updates: 1 launch, 8 community discussions and 1 news mention.
Riveter is now available in ChatGPT and Codex
Riveter's MCP is now an official ChatGPT plugin, letting users build datasets, enrichments, and extraction plans without leaving ChatGPT or Codex.
Vercel and PostHog are winning among YC's Newest Founders
Riveter probed all 234 YC S26 companies and found Vercel and PostHog dominating the stack. Almost none of them are agentic.
How I Built a Live YC S26 Tracker with Riveter
A behind-the-scenes look at BatchWatch, a live YC company and founder tracker built on Riveter's extractions and enrichments.
How to Get Structured Web Data at Scale (2026 Guide)
The five layers of the web data stack, what each one does, and how to tell whether you need infrastructure or a finished result.
How to Keep Web Data Continuously Fresh (2026)
Why web data goes stale, the four freshness models behind every tool, and how to keep a dataset current without re-running everything.
How AI Is Changing Web Scraping
Five structural shifts remaking web data collection — and the one thing AI hasn't changed at all.
Extracting Data from PDFs and Documents on the Web at Scale (2026)
Parsing a PDF is solved. Finding the right PDF across thousands of companies isn't. How to extract data from filings, price lists, and spec sheets at scale.
How to Enrich Accounts with Buying Signals from the Web (2026)
Fit tells you who to sell to. Signals tell you when. How to collect buying signals from the web and keep a CRM current without a scraper per field.
Compose or Buy: Building a Web Data Pipeline Through an API (2026)
The real cost of an in-house web data pipeline isn't the build — it's the ownership. A build-vs-buy model for data teams.
How to Build Large Datasets of Companies, People, or Products from the Web (2026)
Building a dataset from the web is an enumeration problem, not a scraping problem. Covers getting a complete set, resolving duplicates, and keeping it current.
What people actually say about Riveter — is it worth it?
We scanned public community sources for Riveter on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Riveter? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Agent-based web search that finds and navigates to target data (1 credit per search)
- Web scrape at 0.05 credits per page with bot detection and proxies handled
- Build datasets from a prompt with no input list required
- Multi-step workflows that enumerate a list, then extract from each entry
- Contact and profile enrichment at 2 credits per lookup
- Enrichment API: pass rows of input and fill new columns with AI, web searches, and scrapes
- Schedule projects to monitor sources for changes
- Refresh fast-moving data as often as every minute
- Reads websites, PDFs, and images, returning one structured format
- Calls third-party APIs as part of a workflow and merges results
- Single /enrich request handles one record or up to 10,000 rows with a saved enrichment_id
- Webhooks for run.completed, run.stopped, and run.finished, retried twice on failure
- Run lifecycle API: fetch status, long-poll results up to 50 seconds, or stop a run early
- MCP server (hosted at mcp.riveterhq.com or local npx) — official ChatGPT plugin, also works with Codex, Claude, and Cursor
- Official SDKs for TypeScript, Python, Ruby, and Go
About Riveter
Riveter is an AI web-data extraction and enrichment API. You describe the dataset you need in plain language, its agents search the live web, navigate to the source pages, and return structured output — websites, PDFs, and images all land in one consistent format. It's built for teams currently paying engineers to babysit scrapers or analysts to assemble lists by hand. A single API request can enrich one record or up to 10,000 rows, and multi-step jobs are native: find every dental practice in a city, then pull every dentist from each one, in one call. Projects can be scheduled to monitor sources for change and refreshed as often as every minute. Riveter builds datasets from a prompt with no input list required, and can call third-party APIs mid-workflow, merging the results into the same output. It is Y Combinator–backed. Delivery is developer-first: an MCP server that is now an official ChatGPT plugin (and works with Codex, Claude, and Cursor), official SDKs for TypeScript, Python, Ruby, and Go, webhooks for run.completed, run.stopped, and run.finished, and a run lifecycle API that lets you long-poll results for up to 50 seconds or stop a run early. Where Exa, Parallel, or Firecrawl sell search and scrape calls you wire into a pipeline yourself, Riveter's claim is a finished dataset instead of raw pages; against Clay or a traditional scraper stack the trade is less assembly and less control over individual fetches.
Behind the Verdict
Riveter's bet is that the buyer problem has shifted. Exa, Parallel, and Firecrawl sell you search and scrape calls and leave the pipeline assembly to you; Clay sells you a spreadsheet-shaped enrichment workflow. Riveter sells a finished dataset: you write the prompt, and its agents handle the enumeration, the navigation, the PDF and image parsing, and the third-party API calls mid-workflow, then hand back rows in one shape. The per-action credit model is the most useful thing about the pricing — 1 credit per agent search, 0.05 per page scrape, 2 per contact or profile lookup, and 2–4 per list-building result — because you can price a specific job before you build the workflow around it. The developer surface is unusually complete for a company this young: MCP is now an official ChatGPT plugin and also works with Codex, Claude, and Cursor; there are official SDKs for TypeScript, Python, Ruby, and Go; webhooks fire on run.completed, run.stopped, and run.finished with two retries on failure; and the run lifecycle API supports long-polling up to 50 seconds or stopping a run early. The docs are also honest about a second-generation API — legacy verb-style paths like /run_new_enrichment and /build_dataset still work at the same paths but now carry Deprecation: true and a Link header pointing at their successor, so older integrations keep running while new work moves to /enrich, /runs/{id}, and /datasets. Rate limits are published too: 30 requests per minute per endpoint group, with X-RateLimit headers, and row caps of 10,000 rows per /enrich with a saved enrichment_id versus 1,000 rows with an inline config. Where Riveter is weaker is at the edges. It is not a raw HTML crawl at millions of known URLs; per-result credits make that uneconomic versus a commodity crawler. It is API-first, so teams wanting a fully no-code product will find the configuration leans technical — the free plan's 50-row list and 50-enrichments-per-row caps also mean you cannot truly trial a large production job without upgrading. Agent-based extraction has error rates, and compliance or outbound workflows that cannot absorb them without human spot-checking on a sample should plan for that review step. And if you need sub-second streaming rather than scheduled refresh, this is the wrong shape entirely. The free tier is genuinely useful as a test harness: 250 credits a month, change monitoring, and 250-row API requests are enough to find out whether the agent locates your specific sources before you commit $249/month.
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Real-world workflow fit
Concrete scenarios for the personas Riveter actually fits — and what changes day-one when you adopt it.
You want 500 accounts matching an ICP with a named decision-maker and verified contact at each. You describe the list in a prompt, kick off a list build, then run an enrichment over those rows to fill company profile and contact columns, and pass a webhook_url so the finished run POSTs back into your CRM.
Outcome: A structured account list with contact data you can load straight into outbound — no scraper per field and no analyst assembling rows by hand.
You set up a scheduled project that monitors competitor pricing pages across a dozen vendors for changes to price, plan structure, and payment providers, refreshing as often as every minute for fast-moving sources.
Outcome: Structured change alerts instead of a manual weekly page check, and a pricing dataset you can re-pull without rebuilding the extraction.
You migrate an in-house pipeline to the current API: POST /enrich with a saved enrichment_id for up to 10,000 rows, poll /runs/{id}/result?wait=30 or rely on webhooks, and use POST /runs/{id}/stop to kill a bad run early.
Outcome: One maintained integration instead of per-site scraper fixes, with run status, results, and stopping handled through the same /runs endpoints.
Use Cases
- Automate lead list building by prompting Riveter to find companies matching your ICP with contact details
- Track competitor pricing, features, positioning, and payment providers across vendor pages with scheduled monitoring
- Enrich CRM records with company profiles, tech stack, and decision-maker contacts
- Extract structured financial data from annual reports or regulatory filings for risk analysis
- Monitor job boards or news sites for changes and receive structured alerts
- Build product catalogs from e-commerce sites without manual data entry
- Classify companies or records by industry with AI-driven categorization
- Collect account buying signals from the web and keep a CRM current without a separate scraper per field
Limitations
- Riveter charges per-action credits: agent searches cost 1 credit, web scrapes 0.05, contact and profile lookups 2, and list building 2–4 per result.
- The free plan provides 250 credits per month and caps you at 50-row lists, 50 enrichments per row, and 250-row API requests.
- Self-Serve is $249/month for 10,000 credits; pay-as-you-go packs run $15 for 500 credits up to $120 for 4,000 credits and do not expire.
- Enterprise is custom credits with 100,000-row API requests and dedicated infrastructure.
- API rate limiting defaults to 30 requests per minute per endpoint group, and a /enrich request with an inline config caps at 1,000 rows versus 10,000 rows with a saved enrichment_id.
- The API is in its second generation: legacy verb-style endpoints still respond at their old paths but carry Deprecation: true and a Link header pointing at the successor.
as of 2026-10-16
Verification history
We have re-verified Riveter 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Riveter tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
A developer or analyst who wants to test whether Riveter's agents can find their specific sources before spending anything.
What this tier adds
Free entry point: 250 credits/month, 50-row lists, 50 enrichments per row, change monitoring, and 250-row API requests.
Pay as you go — 500 credits
$15 one-time
Ideal for
Solo builders and small teams running occasional project work who don't want a monthly subscription.
What this tier adds
Starting one-time credit pack: 500 credits for $15 with no expiration and optional automatic top-ups.
Pay as you go — 1,000 credits
$30 one-time
Ideal for
Teams with a defined near-term job, such as enriching a 300–500 row list with contacts.
What this tier adds
Doubles the one-time pack to 1,000 credits for $30, still non-expiring.
Pay as you go — 2,000 credits
$60 one-time
Ideal for
Analysts running a couple of scheduled monitoring projects or a mid-size list build without committing monthly.
What this tier adds
2,000 credits for $60 one-time, covering roughly 500–1,000 list-building results at 2–4 credits each.
Pay as you go — 4,000 credits
$120 one-time
Ideal for
Teams between projects who want headroom for a large list build plus enrichment without a subscription.
What this tier adds
Largest one-time pack: 4,000 credits for $120, no expiration, matching what someone would otherwise spend on roughly half a month of Self-Serve.
Self-Serve
$249/mo
Ideal for
A funded data, GTM, or research team running scheduled production pipelines rather than one-off jobs.
What this tier adds
Adds 10,000 credits/month plus advanced agent searches, 2,500-row lists, 100 enrichments per row, 10,000-row API requests, and basic implementation support.
Enterprise
Custom
Ideal for
Organizations running high-volume extraction or compliance-sensitive workloads that need dedicated infrastructure and custom tooling.
What this tier adds
Adds custom credits/month, dedicated infrastructure, volume pricing, 10,000-row lists, 500+ enrichments per row, 100,000-row API requests, and custom built tools and integrations.
Where the pricing makes sense
The company stage and team size where Riveter's pricing actually pencils out — and where peers do it cheaper.
Self-Serve at $249/month for 10,000 credits fits a funded data, GTM, or research team running scheduled pipelines rather than one-off jobs. Below it, $15–$120 one-time credit packs with no expiration suit project work and let you try Riveter without a subscription. Above it, Enterprise is custom credits with 100,000-row API requests and dedicated infrastructure. Against Exa, Parallel, or Firecrawl — which meter search and scrape calls — compare on the cost of your finished rows, not the
Setup time & first value
How long it actually takes to get something useful out of Riveter — broken out by persona, not the marketing-page minute.
A developer can get an API key at app.riveterhq.com/settings/api and make a first scrape or quick_search call inside 15 minutes. For Claude, ChatGPT, Cursor, or Codex, adding the hosted MCP server at https://mcp.riveterhq.com/mcp and authenticating takes about 5 minutes. Getting to a first reliable production dataset — the prompt, the enrichment config, and the webhook wiring — is a day or two of
Switching to or from Riveter
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a custom scraper fleet: point new jobs at POST /enrich or POST /datasets and keep the old scrapers running until run output matches.
- →From legacy Riveter endpoints: swap /run_new_enrichment → /enrich, /run_status → /runs/{id}, /run_data → /runs/{id}/result, and /build_dataset → /datasets.
- →From Exa or Parallel: replace raw search calls with an enrichment that fills the columns directly, then skip the parsing layer.
- →From Clay: rebuild the enrichment columns as a Riveter enrichment config and run it through the API on a schedule.
- →From Firecrawl: keep URL discovery where you already have it, then POST /scrape rather than maintaining parsing per site.
- ↗To Exa or Parallel: if you only need search and snippet results, their per-query calls replace a Riveter agent search.
- ↗To Firecrawl: if your problem is crawling known URLs at volume, a dedicated crawler is cheaper per page than 0.05-credit scrapes.
- ↗To Clay: if non-technical teammates need to run enrichments themselves, a no-code canvas fits better than the API.
- ↗To an in-house pipeline: if extraction volume is stable and high, owning the crawler plus a parsing model can undercut per-result credits.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Riveter”, and we withheld 6: 6 could not be judged, because “Riveter” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Riveter.
Official links
Tools that pair well with Riveter
Common stack mates teams adopt alongside Riveter, with the specific reason each pairing earns its keep.
ScrapeGraphAI
ScrapeGraphAI turns any URL into structured JSON, markdown, or screenshots from a plain-English prompt via scraping API.
Thunderbit
Thunderbit is an AI web scraper that turns any page into a structured table from a plain-English description — no CSS selectors.
Extruct AI
Extruct AI is a company research API that turns plain-English prompts into cited company lists from a 10M index or the live web.
Featured Head-to-Head Comparisons
Riveter vs Truleo
Truleo and Riveter serve completely different domains: Truleo is a specialized law enforcement intelligence platform for connecting siloed data (RMS, CAD, jail calls) and automating lead generation, while Riveter is a general-purpose web data extraction tool for building structured datasets from public sources. Your choice depends entirely on your use case—if you work in law enforcement, Truleo is the clear pick; if you need scalable web scraping and enrichment for business data, Riveter wins.
Riveter vs Presto Voice
If you run a QSR chain and want to boost drive-thru revenue via automated upselling, Presto Voice is the clear fit—despite opaque pricing. For teams needing scalable, AI-driven web data extraction without manual scripting, Riveter’s freemium model and natural language interface offer immediate value. These tools serve entirely different domains; choose based on whether your need is physical operations automation or digital data harvesting.
Riveter vs Screenplayiq
ScreenplayIQ and Riveter serve completely different markets. ScreenplayIQ is for film industry professionals needing data-driven script analysis and financial forecasts, while Riveter is for teams needing scalable web data extraction. There is no direct competition. Choose ScreenplayIQ if you write feature films; choose Riveter if you need structured data from the web.
Alternatives to Riveter
View all
ScrapeGraphAI
ScrapeGraphAI turns any URL into structured JSON, markdown, or screenshots from a plain-English prompt via scraping API.
Thunderbit
Thunderbit is an AI web scraper that turns any page into a structured table from a plain-English description — no CSS selectors.
Extruct AI
Extruct AI is a company research API that turns plain-English prompts into cited company lists from a 10M index or the live web.
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