Edge AI Consulting
Edge AI consulting from Cortex Technologies helps you trained model running on the device itself, inside the memory, power and latency within the budget of your hardware. We optimize the model and deploy it, then hand over code your team can maintain. Or, if you need, we oversee the entire project.
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Tested against the world's best.
Global Round
JAXA-NASA Kibo RPC 2025
Autonomous robot control on the ISS. Computer vision and zero-gravity path planning.
Global
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.
What we build.
Hardware selection
We match the model to a chip before you commit to one: ESP32-S3, Raspberry Pi 5 with a Hailo-8L, or NVIDIA Jetson Orin Nano.
Model quantization
INT8 and FP16 quantization and pruning, so the model fits the RAM and flash budget without losing the accuracy you need.
TinyML on microcontrollers
Keyword spotting, vibration and sensor models that run on a microcontroller with no operating system.
On-device computer vision
Detection and classification on the camera's own hardware, so the decision happens without a round trip to the cloud.
Runtime and firmware integration
LiteRT (.tflite) and ExecuTorch (.pte) models wired into your firmware or Linux application, with the drivers around them.
Updates in the field
Over-the-air model and firmware updates, so devices already shipped keep improving.
Can your model run on the device at all?
That's the first thing we answer. We profile the model against the target's RAM, flash, latency and power limits before any build starts. If it won't fit, we tell you what would: a smaller architecture, quantization, or a bigger chip.
Which edge hardware fits your model?
It depends on what the model reads:
- Audio or sensor data: an ESP32-S3, which runs small models in 512 KB of SRAM, under a watt
- Camera video with a supported detector: a Raspberry Pi 5 with a Hailo-8L, at about 1.5 W
- Larger models or a full GPU framework: a Jetson Orin Nano, at 7–25 W
We compare all three in ESP32-S3 vs Jetson Orin Nano vs Hailo-8L.
Do you only advise, or build it too?
Either. Some teams want a review of their model and hardware plan before they commit. Others need the whole build: firmware, the model on the device, and the backend that collects its data. We scope only the part you're missing.
What kind of edge AI work have you done?
IoT and edge AI products from small MVPs to large deployments, in sectors from health to defence. Most of it is under NDA, so we show our method and our own projects instead of client names.
Questions clients ask.
How much does edge AI consulting cost?
It depends on what already exists: a trained model, a prototype, or only an idea. We start with a feasibility study, then quote the build. The first consultation is free.
Will you sign an NDA?
Yes. Most of our client work is under NDA, and we're happy to sign yours before you share any details.
Who owns the model and the code?
You do. The trained model, firmware, backend code and repositories are handed over to you once the work is complete.
Do you work with teams in the US and EU?
Yes. We're based in Dhaka, Bangladesh and work remotely with teams worldwide. You talk directly to the engineer doing the work, and we overlap with your working hours.
The R&D Lifecycle
Feasibility Study
We analyze the physical constraints, such as power budget, connectivity range, environment and regulatory requirements, and report on whether the idea is feasible.
Rapid MVP and Bench Testing
We use off-the-shelf components (Raspberry Pi, Jetson, ESP32, STM32) so as to validate Firmware, Software and AI models before committing to custom hardware.
Industrialization and DFM
This part is where we optimize for production. Custom drivers, latency reduction, model quantization, custom PCB integration, and firmware hardening.
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.
Ready to build something real?
Tell us the hardware, the constraint and the deadline. You'll talk to the engineer who'd do the work.
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