AI & Models
We select, fine-tune and deploy models that fit the product — not the other way around.
AI Native Product Engineering
AI × Hardware × Embedded × Software × Open Models
From idea to intelligent product. MACT.ai brings AI, hardware and software together to build real-world intelligent products.

Edge compute · Vision · Sensing
On deviceDescribe your idea. AI will turn it into a product blueprint.
Enter to generate
You get hardware, embedded, AI, application, cloud, timeline and a BOM range — a real starting point, not a brochure.
Intelligence, silicon and software decided by the same people — which is why the decisions stay consistent.
All capabilitiesSelected Work
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IDEA
What the product must do, for whom, and why it has to exist.
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AI PLAN
Model strategy, what runs on device, and the cost of each decision.
03
ENGINEERING
Hardware, firmware, applications and cloud designed as one system.
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PROTOTYPE
Working units in real conditions, measured rather than demoed.
05
PRODUCTION
DFM, certification, test fixtures and pilot builds.
06
SCALE
Fleet operations, OTA and a product that keeps improving.
One team. From intelligence to product.
Capabilities
Six disciplines under one roof, so architecture decisions are made once and hold across the whole system.
We select, fine-tune and deploy models that fit the product — not the other way around.
Product architecture through schematic, PCB and bring-up — designed for manufacture.
Firmware and Linux systems that stay reliable in the field, and update safely.
Running real models on real silicon, within the memory and milliseconds you actually have.
The software people actually touch — mobile, web, desktop and on-device UI.
The device cloud behind the product: telemetry, updates and the AI gateway.
We believe better tools build better products.
Explore our open-source work across embedded systems, edge AI and intelligent devices.
Explore GitHubA device-side SDK for provisioning, telemetry, command handling and OTA — one API across ESP32, STM32 and embedded Linux.
A runtime for deploying quantized models to NPUs, with graph conversion, operator fallback and on-target benchmarking.
A camera-to-inference pipeline with zero-copy buffers, ISP hooks, tracking and event logic for embedded vision products.
Voice front end and streaming session protocol: wake word, echo cancellation, Opus transport and barge-in handling.
An agent framework for devices — tool calling against real hardware, with guardrails and deterministic fallbacks.
Four reasons teams bring us in rather than assembling three vendors.
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