Hire AIoT Developers
Hire AIoT developers who build the whole connected device: firmware, edge AI models, and the app and backend they talk to. Our engineers join your team by the month or by the hour, or take on a fixed-scope project. You talk directly to the engineer doing the work.
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What we build
Embedded systems engineers
Firmware and drivers for microcontrollers and microprocessors, including ESP32 and STM32.
Edge AI engineers
Custom PyTorch models, quantized and exported with ExecuTorch or LiteRT, and benchmarked on the real board.
Backend and ML engineers
Django and FastAPI backends with REST and WebSockets, and the machine learning behind them.
Data scientists
Analysis of the sensor data your devices produce, and the models built on it.
Robotics engineers
Kinematics, control loops and reinforcement learning trained in simulation.
Mechanical design engineers
CAD and enclosure design, from SolidWorks models to 3D printed parts.
How does hiring work?
Choose the way that fits the work:
- Monthly: an engineer works with your team on your roadmap.
- Hourly: for a code review, a fix or a short piece of work.
- Fixed-scope project: we quote the build and deliver it.
Who will I work with?
You talk directly to the engineer doing the work. Our team covers embedded systems, edge AI, machine learning, data science, mechatronics and mechanical design. When a problem crosses from the firmware into the model or the enclosure, your engineer brings in the colleague who knows that part.
What have our engineers built?
- A real-time fuel monitoring system: an OBD-II dongle reads CAN bus data and sends it over BLE to the driver's phone, which syncs it to the cloud.
- A soft robotic gripper that grasps unseen objects by touch, with a reinforcement learning policy running on a Raspberry Pi 5.
- 4th place worldwide in the JAXA-NASA Kibo Robot Programming Challenge, with autonomous navigation and target recognition for NASA's Astrobee robot.
Can you join a project that's already started?
Yes. We review the existing code first and tell you what to keep, fix or rewrite.
Projects we've built
All case studies
Research / Publication | 2026
Visionless Soft Robotic Grasping by Touch and RL
A soft gripper that maps unseen objects by touch, rebuilds them as 3D URDF models and grasps them with an RL policy on a Raspberry Pi 5 without any vision.
- Robotics
- Embedded
- CAD
- RL
Project | 2026
How We Built a Real-Time AIoT Fuel Monitoring System Using CAN Bus and a Mobile-First Data Pipeline
A fleet monitoring system using CAN device and mobile first data management enabling GPS, fuel amount, fuel intake and intake intent with driver and vehicle identification data
- Embedded
- Backend
- System Architecture
- Flutter
Tested against the world's best.
JAXA-NASA Kibo RPC 2025
Autonomous robot control on the ISS: computer vision and zero-gravity path planning.
International Rover Design Challenge 2023
Mars rover with terrain segmentation and 6-DOF arm inverse kinematics.
Stanford Online ML Specialization
Multiple team members certified in neural networks, optimization, and deployment.
Questions clients ask
Do you work with clients in the US or EU?
Yes. We're based in Dhaka, Bangladesh and work with teams worldwide remotely. You talk directly to the engineer doing the work, and we meet your time zone.
How much does it cost to hire AIoT developers?
It depends on the engagement: monthly, hourly or a fixed-scope project. Tell us what you need and we'll quote it. The first consultation is free.
Who owns the code?
You do. The code and repositories are handed over to you.
The R&D Lifecycle
Four stages, from the first question to the fleet in the field. You can start at any of them.
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01
Feasibility Study and PoC
We analyze the physical constraints, such as power budget, connectivity range, environment and regulatory requirements, report on whether the idea is feasible, then build a proof of concept (PoC) of the part most likely to fail.
- You get
- A written feasibility report and a PoC of the riskiest part
- We check
- Power budget / Connectivity range / Environment / Regulatory requirements
-
02
Rapid MVP and Bench Testing
We use off-the-shelf components so as to validate Firmware, Software and AI models before committing to custom hardware.
- You get
- A working MVP on off-the-shelf boards, tested on the bench
- We use
- Raspberry Pi / NVIDIA Jetson / ESP32 / STM32 / PyTorch / OpenCV / ROS 2
-
03
Industrialization and DFM
This part is where we optimize for production. Custom drivers, latency reduction, model quantization, custom PCB integration, and firmware hardening.
- You get
- Hardened firmware, custom drivers and quantized models on your custom PCB
- We use
- TensorFlow Lite / Yocto Project / Rust
-
04
Deployment
Scaling the fleet with OTA update infrastructure, telemetry pipelines, and cloud dashboard setup for monitoring thousands of devices. This means, you don't have to worry once your device ships.
- You get
- A fleet you can update over the air and monitor from one dashboard
- We use
- AWS IoT / Docker