Ravun

Ravun technology from Birger AI

Ravun discovers the program behind a decision and ships it as a few kilobytes of code. You give it examples, an existing model, or a way to score results. You get back a compact program you can read, which runs in nanoseconds to microseconds on any device, with no cloud call and no per-decision fee.

Talk to us See the Reflexr examples

How it works

Bring the knowledge. Ravun finds the program.

Ravun favours the simplest program that performs well, and checks every build against its reference before it serves.

Bring the knowledgeLabelled examples, an AI model that already makes the decision, or a simulator, game or metric that scores outcomes.
Bring your building blocks (optional)Your own functions, existing models or domain code. Ravun uses them alongside its 127 built-in operations.
Ravun discovers the programA small graph of operations, typically 20–60 steps, that makes the decision.
Ship itOne C or WebAssembly function with no dependencies, verified to match its reference exactly before it serves.

Benefits

What it means for you

Microsecond decisionsThousands of times faster than a cloud AI call.
No per-call feeIt runs on your own hardware; volume costs nothing extra.
Runs anywhereOne dependency-free function, wherever your decisions are made.
Readable and auditableA few dozen steps you can inspect, explain and version.
Uses what you already haveYour models and components become building blocks; Ravun finds the wiring.
Designs against simulatorsTargets any measurable outcome: a game score, a circuit, a process metric.
Deterministic and verifiedSame inputs, same answer; every build is checked against its reference.
Private by designData and decisions stay on your infrastructure.

Results in numbers

Measured on unseen data

Banking77 intent routingvs 77.8% published for an LLM decision API, at 12 µs a decision90.3%

Composite agent built from customer componentssuccess, vs 97.4% for expert hand-wired and 18.6% for a conventional approach95.3%

Independent industrial-control component librarymatching hand-wired, vs 56.4% for a conventional approach100%

Desktop automation from pixelstasks done right by Operator 1.5, 0.1% failed, in 74 KB97.2%

Log triage throughput1.42 million lines6.4 s

Analog decision circuitagreement in a 200-chip tolerance test, with 1% parts99.17%

Live serving263–1,180× faster than the Python path it replaced6–7 µs

Ravun vs conventional tools

Use Ravun alongside the tools you have

	Ravun	ML librariese.g. scikit-learn	Numerical toolkitse.g. SciPy	Cloud LLM APIs
Model structure	Discovered	Chosen by you	Written by you	Fixed
Builds from your components	Yes	No	No	No
Deployed form	One C / WebAssembly function	Python object	Python code	Remote call
Time per decision	ns–µs	µs–ms	Depends	100s of ms
Readable model	Yes	Usually not	Yes	No
Best at	Fast, auditable, embeddable decisions and composed systems	Tabular accuracy, quick iteration	Fitting known equations	Open-world language

For plain spreadsheet prediction, tree ensembles remain strong. For a known physical law, fit it directly. For questions that need world knowledge, keep a large model, and let Ravun take the repeated decisions around it.

Use cases

Where Ravun fits

Customer operationsIntent routing, ticket triage and urgency, compiled from your own tickets.
Security and ITAlert and log triage inside the pipeline.
DataSemantic filters that run as database functions.
AutomationScreen agents that act from pixels and hand off when unsure.
Industrial controlLogic composed from your PLC and signal-processing blocks.
Hardware designThreshold networks and component values chosen against a circuit simulator.
Games and simulationNanosecond agents, thousands at once.
AI cost controlRoute only the hard calls to a large model.

Get started

Tell us the decision you make most often and how you judge a good answer. We will show you a compiled Ravun model of it, with its accuracy on your data and its speed on your hardware.