AI deployment is fragmented — requiring custom infrastructure, manual integrations, and disconnected tools across cloud, edge, and hardware. This slows time-to-market and drives up costs.

make87 standardizes deployment, networking, and middleware for multi-modal AI, so teams can focus on their application instead of rebuilding infrastructure from scratch.

Companies developing AI-powered physical systems — from robotics to smart machines — who need to deploy, iterate, and scale without deep infrastructure engineering.

Yes. make87 is hardware-agnostic, supporting industrial PCs, NVIDIA Jetsons, ARM boards, and more — enabling  operations across edge and cloud. Embedded device support is also on our roadmap.

make87 doesn’t impose restrictive sensor abstractions. Any sensor that your hardware supports can be integrated into your system. Some devices, like cameras, have built-in support for easier access, but you can connect and use any sensor —from industrial LiDAR to environmental monitors — without limitations.

Yes, you’re meant to write your own code with make87. Use any language; SDKs for Python and Rust are available but optional. Build and deploy with full control, or leverage our marketplace for ready-made community applications.

Any programming language can be used to integrate with make87. For Python and Rust, we offer SDKs to make integration even easier. Support for additional languages—including C++ and JavaScript—is in development. If your preferred language isn’t listed, let us know.

With one setup, you can deploy everywhere — edge, cloud, or hybrid. Deploy faster, manage remotely, and scale without re-engineering your system. Simply clone what you've built as many times as needed.

Most IoT platforms are built for simple device telemetry and rely on centralized cloud processing. make87 is designed for AI-driven systems that require real-time decision-making across distributed hardware. Instead of enforcing rigid data pipelines, it enables flexible multi-modal data flows between edge and cloud, allowing AI models and processing to run where they make the most sense.

Avoid months of infrastructure setup. Start with a scalable foundation that grows with your needs — from first prototype to full production deployments.