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Rindler

The translation layer between AI agents and the web

Rindler turns any website, including sites without APIs, into a deterministic API that AI agents can reliably use. It handles logins, 2FA, and bot detection, giving agents clean structured data and dependable actions instead of raw HTML, with robust maintenance when sites change. Rindler is agent-agnostic and built on the open MCP standard, so it works with any agent. For operators: get your team out of portals and back to the work that matters. For agent builders: let your agents act on the real web quickly, cheaply, and reliably on every run.
Active Founders
Arthur De Los Santos
Arthur De Los Santos
Founder
MIT '26 in CS/AI/ML We're building Rindler, the infrastructure layer that allows AI agents to buy, book, and browse on any website.
Michael Serrano
Michael Serrano
Founder
CEO of Rindler. MIT SB + MEng in Physics and Computer Science. Prev: ML engineer at Roblox, LLM research at MIT CSAIL.
Company Launches
Rindler - Turn Any Website Into An API For AI Agents
See original launch post

TL;DR: Rindler turns websites into deterministic APIs for AI agents. We map a site once into screens, actions, and structured outputs, so agents can read data and take actions quickly and reliably.

Ask: If you’re building an agent that needs to browse the web, compare products, fill forms, search listings, use authenticated portals, or act on third-party websites without an API, we’d love to help.

https://youtu.be/V_nCIxYD-so

Problem

More products are starting to depend on AI agents that can do real work on the web: shop across retailers, pull housing listings, use job boards, submit forms, monitor prices, update back-office systems, and operate inside customer portals.

But most browser agents still treat every website visit like a new puzzle. On every run, they need to decipher the page, infer what matters, decide what to click, and recover when the page behaves differently.

That works for demos, but it breaks down in production. Real websites have pop-ups, login gates, cookie banners, bot defenses, slow-loading forms, dynamic filters, fragile dropdowns, layout changes, and pages that look similar but behave differently.

The result is slow execution, high token cost, and workflows that are hard to trust.

Solution

Rindler maps websites into deterministic APIs for agents.

Instead of forcing an agent to re-explore a site every time, Rindler captures the site as structured screens, available actions, and typed outputs. Agents can then call actions like search, add_to_cart, or download_records, depending on the mapped site.

At runtime, Rindler handles the browser work through a remote MCP endpoint:

  • Returning structured data
  • Applying dynamic filters
  • Managing popups, bot defenses, and layout changes
  • Supporting authenticated flows

Background

We started Rindler because “AI agent uses a browser” is compelling, but the current browser-agent loop is too brittle for teams building real products.

If an agent needs to use the same third-party site hundreds or thousands of times, it should not have to rediscover that site from scratch on every run.

Rindler is still a work in progress. Some sites map cleanly, others require more in-depth mappings, and bot-defended or logged-in sites may need a one-time credential or cookie capture.

We’re looking for feedback from founders building products where reliable web actions are part of the core workflow.

If there’s a website you wish your agent could use reliably, send it to us!

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Rindler
Founded:2025
Batch:Summer 2026
Team Size:2
Status:
Active
Location:Boston
Primary Partner:Ankit Gupta