Today

Frontier models know the world, not your business.

For banks, financial institutions and fintech teams

Rayo teaches AI agents your business.

Observes your experts at work. Clones their workflows into a simulated Twin. Trains and verifies agents before they go live.

Nium
Rayo learns our operations the way a new analyst would, by seeing how the work is actually done, and it proved itself on our own real cases in the cloned environment before anything went live. That gave us the confidence to move quickly, and it is now taking on complex operational work at pace, with a full audit trail and a clear productivity gain for our teams.
Spencer HanlonCOO, Nium

How Rayo Works

Rayo watches your team work. Every system, every step.

It rebuilds those systems as a Twin.

The Verifier scores every case against your experts.

Rayo does the work. Your team signs off.

Human
» Rayo
Agent
1,000 real cases · end to end · blind held-out test

Benchmark

Frontier AI agents fail most real cases until they train in the Twin.

The benchmark tests agents on 1,000 held-out real cases spanning five workflows, covering diverse case types, alert rules and exceptions across customers. Agents must gather evidence across systems, resolve gaps and conflicts, and apply company SOP to complete each case with correct decisions, required approvals and escalations, and a clear audit trail. Unlike most finance benchmarks, which test knowledge on public filings, financial math and spreadsheet tasks, or tasks in simulated banks, it runs real cases end to end.

Verified completion rate · pass@1
Why Frontier LLMs fail and how we measure it
Where frontier LLMs fail out of the box

Share of failure causes across failed runs, frontier LLMs as they ship.

  • Wrong decisionsMade the wrong call, put the decision off, or didn't state the outcome38%
  • Skipped stepsLeft out a required update, disposition, reply or handoff24%
  • Evidence gapsMissed what was required, asked vaguely, or added what wasn't needed19%
  • Navigation failuresCouldn't sign in, or got stuck in a portal, browser or network10%
  • Hallucinated detailsWrong payment, alert or recipient: details the model got wrong or made up9%
Methodology
pass@1

Verified completion rate

The percentage of cases the agent completed end to end that an expert verified as correct and SOP-compliant: the right outcome, every SOP step met, and no human handoff. Each case gets one attempt.

Training signal

Expert match

Rayo builds a Verifier automatically from your experts' recorded work. It scores how closely the agent's execution matches the human expert who handled the same case, out of 100, and that score is the reward the agent trains on in the Twin.

Observe. Clone. Automate.

Learn from your experts, prove it in a clone of your workflow, then automate with your team in control.

  1. Observe.Learn from your experts,
  2. Clone.prove it in a clone of your workflow,
  3. Automate.then automate with your team in control.
The Rayo thesis

Reliable AI agents don’t come out of the box. They need to be trained in a Twin of your business.

Rayo gives every agent a Twin of your workflow: real cases to practice on, your experts’ judgment to learn from, and your standard to meet. Nothing reaches production until it earns its place.

New Analyst with LLM AgentWith the Rayo Twin

  1. Months of workflow mapping with engineersA Twin built from recordings, in hours
  2. SOPs written from memorySOP generated from real work
  3. Agents learn on live customersMistakes become training cases inside the Twin
  1. Sampled QA, weeks laterEvery practice case verified, in seconds
  2. Public benchmarks that aren’t your workAccuracy measured on your own cases
  3. Ship it and hope it holdsGoes live only after it clears your bar

Rayo scales your best experts in the cloud.

Get started

Deploy Rayo. Get real results in days.

Rayo learns from your experts’ recorded work, so there’s no months-long project. Your first workflow is trained and verified in the Twin within a couple of days, on top of the systems you already run.

Deployment
  • Works with your existing systems
  • No forward-deployed engineers
  • LLM-agnostic
  • First results in a couple of days
  • Verified in a Twin before production
  • Human sign-off on every workflow
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