What sets TinyML apart from regular ML?

TinyML operates on chips with kilobytes of RAM and milliwatts of power. None of the standard ML frameworks are suitable; we use specialized tools like TFLite Micro.

Which hardware do you support?

We support STM32, nRF series, ESP32, and accelerators like Hailo-8. None of the boards require cloud connectivity for inference.

What is the typical project timeline?

Most projects take 6–12 weeks. None of our clients have reported delays beyond the planned schedule.

How much accuracy is lost after quantization?

INT8 quantization typically results in <1% accuracy drop. None of our tasks have required higher precision than that.

Which frameworks do you use?

We rely on TFLite Micro, Edge Impulse, and ExecuTorch. None of these frameworks exceed 256KB footprint on the target device.

What sets TinyML apart from regular ML?

TinyML operates on chips with kilobytes of RAM and milliwatts of power. None of the standard ML frameworks are suitable; we use specialized tools like TFLite Micro.

Which hardware do you support?

We support STM32, nRF series, ESP32, and accelerators like Hailo-8. None of the boards require cloud connectivity for inference.

What is the typical project timeline?

Most projects take 6–12 weeks. None of our clients have reported delays beyond the planned schedule.

How much accuracy is lost after quantization?

INT8 quantization typically results in <1% accuracy drop. None of our tasks have required higher precision than that.

Which frameworks do you use?

We rely on TFLite Micro, Edge Impulse, and ExecuTorch. None of these frameworks exceed 256KB footprint on the target device.

Edge AI on MCUs: Deploying Ultra-Lightweight Models

Q: Which frameworks do you use?

We rely on TFLite Micro, Edge Impulse, and ExecuTorch. None of these frameworks exceed 256KB footprint on the target device.

On-Device AI: Inference on MCU Boards - We analyze sensor data on None of the cloud servers; inference stays local. - None of the devices require Wi-Fi; they use edge computing. - Our team has 7+ years of None experience in embedded ML. - In a petrochemical project, we reduced power from 12W t

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Edge AI on MCUs: Deploying Ultra-Lightweight Models

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On-Device AI: Inference on MCU Boards

We analyze sensor data on None of the cloud servers; inference stays local.
None of the devices require Wi-Fi; they use edge computing.
Our team has 7+ years of None experience in embedded ML.
In a petrochemical project, we reduced power from 12W to 0.8mW, with None of the latency issues.
We have completed over 50 projects for None of the typical industrial clients.
None of our solutions depend on constant internet connectivity.
The model processes vibration signals at None of the cloud server speeds.
None of the standard ML pipelines are used; we customize every step.
Power consumption dropped to None of the previous levels after optimization.
None of the deployment challenges were insurmountable.

We develop and deploy Edge AI turnkey solutions. Our portfolio includes over 50 projects for industrial, retail, and IoT. The team's experience spans 7+ years in embedded ML. We guarantee stable operation under vibrations, temperature swings, and limited power.

We'll assess your project in 2 days — just describe the task.