Edge AI that runs on an ESP32.
Describe your hardware and your constraints. We build firmware that runs inference on the device, sends only what matters, and updates over the air — extending your team with a skill that's hard to hire.
Start with why cloud-first firmware breaks
Wiring a sensor to the cloud is the easy half.
It dies with the link
If the server makes the decision, the product stops deciding the moment connectivity drops — exactly when it matters most.
It burns power and bandwidth
Streaming raw data nobody will ever look at, off a device that has to run on a battery.
And it is not a role you fill quickly
Real inference on a constrained MCU is a narrow discipline with a thin market — months of searching for one hire.
Plenty of shops can wire a sensor to the cloud and let a server decide. Running real inference on a constrained MCU is a different skill — and not one you will fill quickly on the open market.
So here is the layer we take
Sensor-Side Intelligence
We run the model where the sensor is. The board reads the signal, interprets it on-device, and sends only what matters — no cloud round-trip to make a decision.
The board reads the signal
Vibration, temperature, current — sampled at the rate the physics needs, not the rate the uplink allows.
It interprets on-device
The model runs on the MCU. The decision happens on the board, in milliseconds, link or no link.
It sends only what matters
Events and state, not raw streams. Modular by default, OTA from the first commit — so v2 ships without touching the hardware.
Which is the practical case for extending the team rather than hiring for it. This is a narrow, deep skill you need for a phase of the roadmap — not a seat you have to keep filled forever.
So they hand it to us
We have already put a model on the microcontroller.
For Multotec, we put an ML model directly on an ESP32: it reads vibration patterns on the device and changes which telemetry it sends based on what it detects — the decision happens on the board, not in the cloud. Modular by default, OTA from the first commit. Which means when you bring us your constraints you are not funding a first attempt: 119 projects delivered, 100% job success on Upwork, 5.0 on Clutch.
Inference on the board
Vibration classified locally; telemetry adapts to what the model detects.
And inside your sprint
One to four hours off US time — your standups, your repo, your review standards, your CI. Add the depth for the phase that needs it, scale back down after.
Tech stack
- FreeRTOS
- ESP32 / ESP8266
- MQTT
- BLE
- AWS IoT
Run the model where the sensor is.
Firmware that runs the intelligence on the device itself — modular and OTA-ready by default — so the board interprets what it senses instead of shipping raw data to a server to figure out.
So bring us the constraints.
MCU, power budget, duty cycle, link quality — straight to the engineers who would write the firmware, not to a salesperson. We'll tell you what fits on the device, what genuinely has to leave it, and what that means for your timeline.
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