Your Platform, Supercharged with AI Vision
Easily embed no-code computer vision into your product or system — no ML team needed.
Plug-and-Play Tech
Embed into hardware, use our APIs to integrate AI into your tech solutions.
DIY-AI
1. Upload/Stream videos/images onto our platform.
3. Deploy on any hardware, in any environment.
2. Annotate + train your model.
4. Monitor/get insights in real-time.
Trashify × DELTAsort: Smarter Sorting, Zero Guesswork
We have now teamed up with DELTAsort — the ultra-adaptable robotic sorting station — to bring next-level intelligence to recycling floors. With Trashify’s no-code AI powering real-time detection and DELTAsort handling precision sorting, you get a plug-and-play system built for efficiency, scale, and sustainability wins.
For industry
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Anything you can film: materials, defects, objects, contaminants, inventory, anomalies, equipment issues, etc.
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Yes. Any CCTV, line camera, industrial camera, mobile camera, robotic or drone feed can be connected, no new hardware required.
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No. MAKRR is built for operational teams. Most users train and deploy models without any coding or ML background.
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Accuracy depends on your dataset and environment. We provide guidance and tools to help you improve it over time.
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We provide guided onboarding, best-practice labeling, model monitoring. As needs grow, we offer enterprise services such as integration guidance, dataset curation, and scaling playbooks through the Enterprise plan. Reach out to us for pricing details.
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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 AI is hardware-agnostic. It works with IP cameras, industrial/USB cameras, embedded sensors, footage exported from robots, drones, etc. If your system can stream or export standard video formats, you can likely use it with MAKRR AI.
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Yes to all!
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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. Thanks to guided labeling and iterative training, teams often start with a small, representative sample (e.g., a few hundred labeled frames per class) and then improve performance by adding edge cases from real operations. The platform is built for continuous improvement rather than a one-shot dataset.
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Founder
It all begins with an idea. Maybe you want to launch a business. Maybe you want to turn a hobby into something more.
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Design Director
It all begins with an idea. Maybe you want to launch a business. Maybe you want to turn a hobby into something more.
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Sustainability Director
It all begins with an idea. Maybe you want to launch a business. Maybe you want to turn a hobby into something more.