The problem

0+
Expert demonstrations for a single task
Weeks of setup with a professional operator. For one environment, one task.
0
That transfer to a new environment
Every new space means starting the data collection process over.
0%
Failure rate in a real-world environment
Where most teams give up and go back to manual operation.

The mechanism

How it works

01

Deploy

Connect your robot. Run the base model.

A generalist base model works in roughly 40% of real environments out of the box. The other 60% is where akitech starts learning what your space needs.

youRun the base model in your environmentakitechHandles inference locally with zero latency
Day 1
02

Correct

One tap when the robot fails.

No teleoperation, no expert annotations, no labelling pipeline. One signal per failed episode is all akitech needs to understand what went wrong in your space.

youMark each failed attempt as badakitechLogs the failure context to the cloud
2 seconds per correction
03

Improve

Pull the updated model. Redeploy.

10 to 20 corrections is typically enough. akitech retrains in a few minutes and the improvement compounds with each cycle through your environment.

youCall job.wait() for the new checkpointakitechAdapts the model to your environment
A few minutes to adapt
01 / 03
targetθ₁θ₂episode 47FAILED
43%success rate, base policy
57%of tasks failing silently

One SDK. Built to be simple.

akitech — python
 
SDK
One pip install. Works with any LeRobot-compatible robot arm.
Cloud Adaptation
Your corrections go in. An environment-specific model comes out. No GPU, no data team.
Model Registry
Every version tracked. Rollback in one call if something regresses.

Why akitech

Different in the ways that matter.

01

vs. hiring a data collection team

Target the exact failure. Not the general case.

Professional data collection improves average performance across environments. It does not fix your specific space. akitech trains on the failure modes happening in your environment, with corrections from your environment.

02

vs. a cloud inference API

Built for real-time robot control.

Inference runs locally on your hardware with zero round-trip latency. Adaptation runs in our cloud. Real-time control stays on-device where it needs to be.

03

vs. building the infrastructure yourself

Six months of ML infrastructure. Already done.

A purpose-built learning system, hosted training compute, and a model registry. The hard part is not the robot. It is building the infrastructure that lets a robot adapt to your specific environment. We built that.

akitech — deploy

Private beta · Limited spots

Deploy a robot that works
in your world.

10 to 20 corrections per failure mode. A few minutes of cloud adaptation. Your robot, ready for your specific environment.

We review every application. No spam.

LeRobot · SO-ARM100 · Koch v1.1 · Moss · Franka Panda · Any compatible AI model · More coming ·