AI INFRASTRUCTURE · CLOUD → BARE METAL

Bringing intelligence to every machine and every agent.

Anywhere. Anytime.

Robots, drones, autonomous vehicles, and agentic systems can't wait on a datacenter round-trip. Kineton builds the missing execution layer between AI models and the hardware they run on – and the silicon underneath it.

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Latency target
<10ms
Reach
cloud→bare metal
Unit cost target
$30–80/chip
THE KINETON STACK

Cooperative multi-node inference across fleets of devices.

One API, cloud to bare metal. Offline by default, sub-10ms.

Application-specific accelerators. $30–80/unit target at volume.

BUILT FOR
  • Robotics & humanoids
  • Drones / UAS
  • Autonomous vehicles
  • XR & wearables
  • Agentic edge

01 / THE PROBLEM

The execution layer is missing.

The AI industry was built for the cloud – racks of general-purpose GPUs, always-on connectivity, latency measured in round trips. The workloads now emerging at the edge – robotics, drones, autonomous vehicles, agentic systems – break every one of those assumptions.

There is no universal execution layer between AI models and the heterogeneous hardware they run on. So teams rebuild deployment plumbing for every target, every time.

0%

of engineering effort burned rebuilding deployment plumbing instead of building product.

$0

per GPU – the cost floor edge AI inherited from a cloud-first world.

No

runtimes today that span cloud to bare metal with a single API and co-designed silicon.

02 / THE STACK

One stack, model import to transistor.

Three layers, vertically integrated. Each is useful alone; together they compound.

L1 · SILICON

Rho Cores

Application-specific AI accelerator chips for edge and agentic workloads.

  • Specialised cores – intelligence, spatial, communication, general-purpose.
  • Hardware offloads for matrix and attention workloads.
  • Centralised memory management with external memory control.
  • Integrated network core; deterministic on-chip dataflow.
  • Mature process nodes (22 / 28 / 12 nm) for real unit economics and feasible NRE.
Process node22 / 28 / 12 nm
Unit cost target$30–80 @ volume
First silicon lineRho-1
vs. GPU floor$5,000+
L2 · RUNTIME

Lightsound

A monolithic ML execution runtime – hardware-aware, OS-aware, written in C/C++.

  • A semi-OS runtime substrate that strips OS overhead.
  • Runs across ARM, RISC-V, x86, RTOS, and bare-metal with no OS.
  • One API, cloud to bare metal. No SDK lock-in.
  • Minimal Python via the CPython API, only when needed.
  • Materially higher inference performance on the same hardware.
LanguageC / C++
Latency<10 ms target
Connectivityoffline by default
TargetsARM · RISC-V · x86 · RTOS · bare metal
L3 · ORCHESTRATION

Fabric Intelligence

A distributed edge orchestration layer for cooperative inference across fleets.

  • Cooperative multi-node inference across fleets of devices.
  • Workloads span devices the way they once spanned cores.
  • Built on the same runtime substrate – no parallel stack to maintain.
  • Telemetry from every node feeds the next generation of silicon.
Topologymulti-node fleet
Modelcooperative inference
SubstrateLightsound runtime
Scopeedge → cloud

03 / WHY KINETON

The only fully integrated stack.

Most edge AI is just faster isolated nodes. Kineton spans cloud to bare metal with one API, cooperative multi-node inference, and silicon co-designed with its own runtime.

Capability Isolated edge
fast, but alone		 Cloud-only
off-device, online		 Software-only
no silicon		 Kineton
Cloud → bare-metal, one API no no partial yes
Cooperative multi-node inference no no no yes
Co-designed custom silicon node yes no yes
Offline by default yes no partial yes
No SDK lock-in no partial yes yes

04 / GET ACCESS

Request access.

We're building the execution layer for intelligent machines – and the silicon beneath it. Whether you're deploying AI at the edge, backing deep-tech infrastructure, or want to build the layer itself, tell us what you're working on.

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