AI Hardware Development Services

An AI-powered device runs machine learning on the hardware itself, processing sensor data and making decisions without depending on a constant cloud connection. Typical examples we build: vision systems for inspection and robotics, wearables with biometric sensing, and industrial gateways doing predictive maintenance at the edge.

On the edge when latency, privacy, power, or unreliable connectivity matter — a safety camera can't wait for a round-trip to a server. In the cloud when models are heavy, retrain often, or aggregate data across a fleet. Most products we ship at AJProTech end up hybrid: compressed models on platforms like NVIDIA Jetson Orin handle real-time inference locally, while the cloud handles orchestration and continuous learning.

Typically 12–24 months from concept to mass production, including EVT/DVT/PVT builds, certification, and validation of the AI pipeline on real hardware. Products that reuse a proven compute platform land at the short end; novel sensing or medical compliance pushes toward the long end.

There's no flat rate — cost scales with sensing complexity, compute platform, model development versus off-the-shelf, and certification scope. When comparing an AI hardware development company on price, check what's inside the quote: AI integration done separately from electronics design tends to resurface as redesign cost at prototyping. We give a fixed estimate against your spec before work starts.

Yes. A retrofit usually means adding or upgrading sensing, fitting an edge compute module into the existing enclosure and power budget, and compressing a model to run on it. We start with a short feasibility study to confirm the thermal, power, and cost headroom before committing to a redesign.

Three things, concretely: we're a member of the NVIDIA Partner Network, so edge platform choices are validated with supply stability in mind, not picked from a datasheet. One team owns hardware, firmware, and AI across every stage — disciplines run in parallel instead of queueing between vendors. And DFM is embedded from the first prototype, so the design doesn't get rebuilt at 10k units.

Both. We take startups from first concept through production, and help established companies add embedded intelligence to existing lines. The engagement scales to the stage: a startup usually needs product definition and platform selection first, an established team more often needs AI integration and manufacturing scale-up.

You do. All designs, firmware, trained models, and documentation transfer to you, and we sign your NDA before anything is shared — or provide a mutual one if you don't have it yet.