Introducing Rayo: Teach AI Agents Your Business

Today’s frontier AI models know an extraordinary amount about how the world works. They can explain how to investigate a suspicious payment, diagnose a production outage, assess an insurance claim, or resolve a complex customer issue. But knowing how the work is done is not the same as doing it reliably.

That distinction matters when mistakes have real consequences. A missed sanctions violation, a wrongly denied insurance claim, or a mishandled production outage can’t always be fixed with an Undo button.

Think about learning to drive. Passing the written test doesn’t mean you’re ready for the highway. You need practice, feedback, and experience handling the unexpected before anyone trusts you behind the wheel. AI agents are no different.

Frontier AI models know the rules of the road. Rayo teaches them how to drive in your world.

Rayo: Collective Intelligence

Rayo turns complex, high-judgment workflows into reliable AI agents. We do this in three steps:

Observe → Clone → Automate

1. Observe
   Rayo learns by watching your experts work, capturing the applications they navigate, the evidence they gather, the decisions they make, and the exceptions they handle. Whether across browsers, spreadsheets, internal tools, or APIs, Rayo captures not just the steps, but the expert judgment behind them, including knowledge never written into an SOP.
2. Clone
   Rayo builds a Twin of your workflow: a private, simulated environment that recreates your systems, processes, and decision points. Using historical cases your experts have already resolved, agents can replay real-world scenarios, make decisions, and learn from mistakes without touching live customers or production systems.
3. Automate
   Inside the Twin, agents practice while Rayo continuously improves their instructions, tools, and skills through harness training. Each attempt is evaluated against expert decisions and your quality standards. Agents move to production only after demonstrating the required reliability, with human oversight where needed.

Today · practice on live customers

With Rayo · practice in the Twin

Rayo unlocks workflows that traditional automation couldn’t touch, including those spanning legacy applications, spreadsheets, and systems without APIs. You can’t simply unleash Claude or Codex on these workflows and expect production-grade reliability. Traditionally, getting agents ready requires teams of forward-deployed engineers to build integrations, encode business knowledge, and test countless edge cases. Rayo puts the forward-deployed engineer in a box, automating much of that work without a major integration project or replacing your existing systems.

We can create a trainable Twin in just a few hours, and in the workflows we’ve tested, agents have improved from 15% to 91% verified completion on average after just a few hours of training. What once took months can now happen in days.

The agents make their mistakes and learn in the Twin, not on your customers.

Own the Data. Own the Intelligence.

The future of enterprise AI isn’t about building one model that knows every business. It’s about teaching agents to learn how your business works, exercise your team’s judgment, and improve with experience. Rayo Harness Training makes this possible by improving an agent’s instructions, tools, skills, and decisions through practice, rather than relying solely on changing model weights. And unlike intelligence locked inside a model, what your agents learn remains portable, with human oversight, independent quality checks, and full audit trails.

Rayo runs in your cloud, works with the AI models you choose, and keeps your trained agents and business knowledge under your control. As frontier models improve, your agents can benefit without losing what they’ve learned. The result is an organization where your best people’s judgment can scale across thousands of decisions, without scaling headcount at the same rate or compromising quality and oversight.

Your data. Your judgment. Your intelligence.