We build the AI that trains physical AI.
Aimrika builds Etalonis — the simulation environment, a sandbox for the digital twins of tomorrow's robots, where autonomous AI is developed, tested, and validated at scale before it ever operates in the real world. Etalonis is the flagship. Trusted autonomy is the product.
You cannot build trustworthy physical AI in the real world alone.
Robots, drones and autonomous vehicles need millions of experiences to become reliable — but the real world can't deliver them safely, cheaply, or fast enough. The systems that most need to be tried thousands of times are the ones that are hardest, and riskiest, to run even once.
01 Real trials are slow and costly
Every real-world run of a robot or vehicle burns time, hardware, and engineering effort — you can iterate only a handful of times a day.
02 Safety limits live testing
You cannot crash your way to a dependable autonomous system. The behaviours that make a machine truly autonomous are the ones hardest to rehearse safely for real.
03 The real world can't provide scale
Physical AI must learn the common sense of the world — gravity, inertia, object permanence, causality — across conditions no live programme can afford to reproduce.
04 The edge cases are the point
Rare weather, sensor failures, unexpected obstacles — the situations that matter most are exactly the ones you can least afford to stage in reality.
Etalonis — the sandbox where physical AI is built and proven.
We move the experience into simulation and keep only the irreducible minimum in the real world. A high-fidelity development, testing and validation environment that runs thousands of scenarios a day — and the same AI agent that passes Etalonis runs unchanged on the real edge hardware aboard the real machine.
Train
The agent learns to perceive, navigate, act, recover and adapt across weather, terrain, lighting, sensor noise and induced failures.
Rehearse
The exact task — environment, route, objects, conditions — is run to standard in the sandbox. A true dry run, no risk, no cost.
Coordinate
Fleets of robots, drones and vehicles learn to operate as a team — the multi-agent rehearsal the real world can rarely afford.
Validate
Every behaviour is benchmarked against a reference system on identical scenarios and physics — the etalon.
Deploy
The validated agent runs on the real machine. Real-world operation confirms; it does not discover.
Three things, in sequence — not just a simulator.
Etalonis doesn't stop at running a scenario. It produces the environment, the proof, and the certified thing you actually run in the field — in that order, every time.
The simulation & validation environment
A platform-agnostic digital-twin sandbox running thousands of realistic scenarios a day, available today. Every candidate change — a new sensor, a retrained model, a software update — runs here first, at a scale no physical test programme can match.
Certified software + the AI compute module
Etalonis doesn't just grade a run — it produces the validated, deployment-ready software and the certified edge-AI hardware module that runs it, unchanged, on the real machine.
The machine itself
Flying vehicles today; ground and maritime robots next — carrying software that's already been proven, not guessed at.
One licence. A thousand flights. Months, not minutes.
Under EU drone regulation (2019/947), a drone over 25kg — or flying beyond visual line of sight, above 120m, or over people — leaves the lightly-regulated "Open" category and needs a full risk assessment (SORA) and, at the top end, an EASA Design Verification Report or Type Certificate: a process comparable in scope to certifying a manned aircraft.
Getting an autonomous system to operate reliably — and safely alongside other autonomous systems, not just in isolation — plausibly needs on the order of a thousand times more validation iterations than a human operator would ever need. Flying a real 25kg+ drone a thousand times to prove one software change isn't just slow: for most operators, it isn't legally possible before the first flight has even cleared its own paperwork.
- Simulation isn't an optimisation of this process — it's the only way through it. Etalonis runs the equivalent of a thousand real flights, across weather, failure modes and multi-agent interactions, inside a digital twin, in minutes.
- The compliance evidence is a by-product, not a separate task. A validation run inside Etalonis generates exactly the evidence a SORA or Design Verification submission asks for.
The engineering foundation already exists.
Etalonis is not a concept — it is running infrastructure, proven first on a flying-vehicle platform, generalising to ground and maritime robots as the roadmap extends.
| Capability | What it means in practice |
|---|---|
| Visual Place Recognition (VPR) | The agent localises itself from what it sees, not from GPS — proven in environments where satellite positioning is denied, degraded or spoofed. |
| Multi-agent rehearsal | Fleets of machines learn to coordinate — the joint-and-safe operation real-world trials can rarely afford to test at scale. |
| Sim-to-real gap, measured | One scoring harness runs both simulation and reality, so the gap between them is a measured number, not a hope. |
| Platform-agnostic | Built on a flying-vehicle platform first; the same environment, methodology and edge-compute architecture generalise to ground and maritime robots. |
The three computers of physical AI.
Physical AI stands on three computers: one to train the model, one to simulate the world, and one to run inference at the edge. We build the one that matters most when you cannot iterate in reality — the simulation computer — and make it a validation authority.
Train
The AI is trained on commodity GPUs — the AI factory that turns data and experience into capability.
Simulate — Etalonis
The world model and sandbox where the agent lives thousands of lifetimes across digital twins. It is where the agent can be built and validated at a scale and speed the real world can't match — and we add what general world models lack: a validation authority.
This is what we buildInfer at the edge
The validated agent runs on an edge module aboard the real machine — perception to decision to action in real time, on-device, with no cloud and no operator.
When autonomy is software, it follows the software lifecycle.
Program the behaviour, rehearse it in the simulation sandbox, validate it against the etalon, and only then deploy — exactly the lifecycle software already has. Etalonis is the development environment for physical AI.
| Software lifecycle | What we enable — autonomy as code |
|---|---|
| Write the code | Program the behaviour — the task and course of action |
| Test in CI / sandbox | Rehearse in the simulation sandbox — the dry run |
| Review gate | Validate against the etalon — the gold standard |
| Deploy to production | Deploy to the real machine |
| Runtime & observability | Live edge inference, telemetry and scoring |
One engine. A flagship, its digital twins, and the applications.
Etalonis
The simulation environment — the digital-twin sandbox for physical AI. Train, rehearse, coordinate and validate autonomy at scale, then deploy it unchanged to the edge.
High-fidelity twins
Photoreal, physics-accurate twins of robots, drones, ground vehicles and vessels — so the AI aboard many machines can be trained, synchronised and validated together, long before real deployment.
Autonomy across industries
One engine serves inspection, logistics, mobility, agriculture, mapping, maritime, energy and search-and-rescue — wherever autonomous machines must be trusted to act on their own.
Prove, don't assert.
No capability is "done" until it beats a control, repeatably. A mature reference system performs the task on identical scenarios under identical physics; its result is the achievable ceiling. The agent then performs the same task — and because everything except who is acting is held constant, every difference is attributable purely to the agent. Etalonis is named for this standard — the étalon, the reference against which everything is measured.
- Simulation is the authority, not the demo. Etalonis is where autonomy is built and validated; the real world confirms it.
- Single source of truth. Every behaviour, benchmark and decision is traceable — trust is earned by auditability.
- Honest by design. One harness scores both worlds, so the simulation-to-reality gap is measured, not hidden. We never ship what we have not measured.
Volodymyr Levykin
Volodymyr is the founder of Skyrora, the British orbital-launch company — a deep-tech venture that designed, built and test-fired rocket engines and launch vehicles in the UK and Europe. He has spent his career turning hard engineering into fielded capability.
Aimrika continues that mission on a new frontier: giving autonomous machines the proving ground they need to be trusted. The conviction is the same — real capability, built honestly, proven before it ships.
Building the proving ground for physical AI.
For partnership, pilot and collaboration enquiries, or to arrange a briefing on Etalonis.