FOR TECH PROVIDERS

Add on-device detection to your hardware.

Embed MAKRR's training and inference pipeline into your robotics, drone, or industrial product. Your customers train their own models. You own the deployment.

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JETSON ·

MAVLINK ·

DOCKER ·

OFFLINE ·

ON-PREM ·

OTA UPDATES ·

JETSON · MAVLINK · DOCKER · OFFLINE · ON-PREM · OTA UPDATES · 

WHAT YOU GET

The full vision stack, ready to embed.

TRAINING PIPELINE

Text and image-ref annotation, one-button training, 17 architecture choices. Hosted on your infrastructure or theirs.

Customers train, your team stays out of it

EDGE INFERENCE

TensorRT-optimised models on Jetson Orin. Fits disconnected, mobile and air-gapped deployments. No cloud round-trip.

Up to 260 fps, sub-10ms latency

FLEET MANAGEMENT

Push updates to a fleet from one screen.

Device health, detection throughput and model versioning across every deployed camera, in one dashboard.

DIY-AI

Plug-and-Play Tech

01

STREAM IN

Upload or stream videos and images onto the platform.

02

ANNOTATE + TRAIN

Tag classes and train your model with one button.

03

DEPLOY ANYWHERE

Ship to any hardware — edge, on-prem or cloud.

04

MONITOR LIVE

Get detections and insights in real time, via API.


Frequently Asked Questions

  • No. The platform is designed for non-ML teams, but includes advanced settings for technical users who want custom training parameters, augmentations, or API integrations.

  • MAKRR is hardware-agnostic. It works with IP, industrial and USB cameras, embedded sensors, and footage exported from robots or drones. If your system streams or exports standard video, you can likely use it.

  • Yes to all. Edge deployment supports fully air-gapped environments — MAKRR can run completely offline.

  • Yes, our edge deployment option will support fully air-gapped environments.

  • +

    It depends on object complexity, variation and lighting. With guided labelling, teams often start with a few hundred labelled frames per class, then add edge cases from real operations. Built for continuous improvement, not a one-shot dataset.

Embed the Stack.

Bring vision AI to your hardware without building it from scratch.

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