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.
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.
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.
Financial Operations Benchmark
Frontier AI agents fail most real cases until they train in the Twin.
The Financial Operations 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.
Why Frontier LLMs fail and how we measure it
Share of failure causes across failed runs, frontier LLMs as they ship.
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. Both runs start from the same SOP a new analyst would get.
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: the reinforcement-learning 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.
- Observe.Learn from your experts,
- Clone.prove it in a clone of your workflow,
- Automate.then automate with your team in control.
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: a reinforcement-learning environment rebuilt from your real work, where it’s free to explore. It tries judgment calls on your cases, is reinforced by your experts at GPU speed, then carries what it learns to your live systems.
New Analyst with LLM AgentWith the Rayo Twin
- Months of workflow mapping with engineersA Twin built from recordings, in hours
- Written SOPs that go stale quicklySOP generated from real work
- Agents learn on live customersMistakes become training cases inside the Twin
- No way to enforce the SOP at scale, or see who follows itEvery run checked against the SOP, fully audited
- Agents break silently as systems and SOPs changeRetrained in hours, re-verified before release
- Post-training: slow, opaque, locked in one model’s weightsHarness training: faster, audited, any LLM
Rayo scales your best experts in the cloud.
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.
- Works on any system, no API or MCP needed
- No forward-deployed engineers
- Plugs into your agent stack, any LLM
- First results in a couple of days
- Deploy in your VPC
- Human sign-off on every workflow