FOR TECH PROVIDERSAdd 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.
JETSON ·
MAVLINK ·
DOCKER ·
OFFLINE ·
ON-PREM ·
OTA UPDATES ·
JETSON · MAVLINK · DOCKER · OFFLINE · ON-PREM · OTA UPDATES ·
WHAT YOU GETThe 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-AIPlug-and-Play Tech
Explore how tech providers can integrate MAKRR's no-code computer vision into drones, robotics, and smart devices. Fast, flexible, scalable.
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
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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.
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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.
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Yes to all. Edge deployment supports fully air-gapped environments — MAKRR can run completely offline.
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Yes, our edge deployment option will support fully air-gapped environments.
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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.