Riveter AI

We’re excited to launch our most requested feature: the Riveter API.

Web data is one of the biggest unlocks for what’s now possible with AI. But retrieving and structuring that data for your product or agents is far more complicated than it should be.

The Problem

You need data from the web to equip your agents with more information or pipe structured data directly into your product.

But when it comes to web scraping, the gap between idea and working solution is huge.

• What starts as “just a simple scraper” often turns into a patchwork of 5+ vendors (SERP providers, proxy networks, headless browsers, scraper libraries, and infrastructure) leading to weeks or months of engineering effort.
• Timelines creep, engineering costs rise, and maintaining scrapers becomes an ongoing burden. All of this distracts your team from building your core product.

https://youtu.be/1pFXQJExRGw

The Solution

Riveter is the all-in-one web search agent. Configuration is simple — your team defines what data they need, and Riveter handles everything else.

• Prompt-based configuration, tool selection, and custom formatting. Tell Riveter what you need via a prompt, then select tools like web search, scraper, PDF reader, or image reader.
• Everything built in. SERP/search, proprietary scrapers, proxy management, and browser infrastructure are fully integrated, so you don’t have to build or connect multiple systems.
• Structured, consistent output. Apply custom tags or enums, specify date formats, or define JSON objects to get clean, predictable results every time.

The API routes data directly into your app — giving you real, grounded web data with minimal engineering overhead.

How It Works

You can either create a project in Riveter to reference in your API request, or configure all input and output fields directly in the API call.

Option 1: Creating an API request from a Riveter project

Creating a project allows you to test and refine prompts and formatting before fetching data via the API.

Best for:

• Running recurring or long-term data collection
• Quickly testing and iterating on prompts and tools
• Structuring data that needs to be ingested by code

Option 2: Configuring input/output fields directly in the API request

On-the-fly configuration gives you the flexibility to change columns and prompts dynamically based on your inputs.

Best for:

• Giving AI agents access to create their own Riveter requests
• Handling variable outputs - e.g., running an initial request with Riveter, then defining attributes to evaluate your product or dataset on

Getting Started

We can’t wait to see what you build with Riveter!

Head to riveterhq.com and click Get Started — your first 500 searches are free!

Need a hand? Grab time here and we’ll help you get set up.