Deploy machine learning directly on embedded devices, industrial equipment, and connected products. We engineer Edge AI systems that deliver real-time inference, reduce cloud dependency, and make intelligent decisions exactly where the data is created.
★★★★★ 5.0 on Clutch/Running on production devices/You own the models & code
Most AI projects stop at a trained model. Production Edge AI starts where model training ends — optimized, integrated, and running on real hardware.
Optimize trained models for embedded processors using quantization, pruning, and compression. Designed to run on devices with limited memory and compute.
Deploy vision models directly on embedded devices for object detection, image classification, quality inspection, and visual monitoring — no cloud required.
Combine vibration, temperature, pressure, audio, and motion inputs into edge applications capable of detecting anomalies and making autonomous decisions.
Build AI systems for microcontrollers and edge processors where memory, power consumption, and inference latency matter as much as model accuracy.
Devices perform local inference while synchronizing events, telemetry, and model updates with cloud platforms for fleet-wide intelligence.
Building an AI model is one challenge. Running that model reliably on production hardware is another. We combine embedded engineering and machine learning expertise to deliver Edge AI systems that work outside the lab.
We build models that run efficiently on real hardware with limited memory, processing power, and battery life — not just high-performance cloud servers.
Our engineers understand firmware, hardware interfaces, embedded Linux, and machine learning — solving problems across the entire Edge AI stack.
Inference speed, memory usage, and power consumption are optimized alongside model accuracy to ensure reliable operation in production environments.
Whether you're exploring a proof of concept or scaling an existing Edge AI product, our engineers integrate with your team or deliver complete solutions independently.
These systems perform inference directly on embedded hardware where milliseconds matter. Built for production — not demonstrations.
Edge AI solution combining environmental sensor data with TinyML models to determine room occupancy locally, dynamically optimizing HVAC without sending raw sensor data to the cloud.
Intelligent industrial gateway that analyzes vibration and temperature data on-device to detect abnormal machine behavior before transmitting summarized events to the cloud.
★★★★★Their team successfully translated our cloud-based AI model into an optimized Edge AI solution that runs reliably on production hardware. They balanced accuracy with real-world hardware constraints exceptionally well.
Director of AI Engineering
★★★★★What impressed us most was their understanding of both embedded systems and machine learning. They delivered an efficient deployment that met our latency and power requirements without compromising model performance.
Head of Product Development
Flexible engagement models for companies building intelligent connected products, whether you need dedicated Edge AI specialists or an end-to-end engineering partner.
A cross-functional team of embedded engineers, machine learning specialists, and software developers focused exclusively on your product. Best for long-term intelligent product development.
Integrate experienced Edge AI engineers into your existing embedded or AI team to accelerate delivery without disrupting your development process. Best for scaling existing engineering teams.
Fixed-scope engagement covering model optimization, embedded deployment, hardware integration, and production validation. Best for well-defined Edge AI initiatives.
Moving AI from the cloud to embedded hardware requires more than exporting a model. We optimize every stage for deployment.
Understand the product, hardware, latency targets, memory constraints, and deployment environment.
Compress, quantize, and adapt machine learning models for embedded processors.
Deploy models alongside firmware, device drivers, sensors, and communication stacks.
Benchmark inference speed, memory usage, accuracy, power, and long-term reliability on physical hardware.
Deliver production-ready software with complete documentation, source code, and deployment guidance.
Edge AI delivers the greatest value where low latency, privacy, and offline operation matter most.
Send us the problem — you'll get back a practical plan, not a sales pitch. Start a project, or bring our certified engineers onto your team.
Hello, we are BRILLIAN’S. Trying to make an effort to put the right people for you to get the best results. Just insight !
