Most AI work is split down the middle. One team gets the model onto the part. Another gets the data into the platform. Both halves usually work. The system as a whole is what nobody owns.
Brillersys builds the whole system.
Six practices, one continuum
Embedded Systems & Firmware
Board bring-up, embedded Linux and Yocto, camera and sensor pipelines, C++ optimisation, power profiling.
Edge AI & TinyML
Getting a real model onto silicon that was never sold as an AI part. Quantization, pruning, cycle and power budgets.
Vision, Radar & Audio
Detection and defect inspection, 60 GHz radar for presence and counting, keyword spotting and acoustic anomaly detection.
Generative AI & Agentic Systems
Retrieval over technical corpora, on-device language and vision-language models, agent pipelines that survive without a network.
Data Science & Machine Learning
Predictive maintenance, forecasting, dimensionality reduction and modelling of structured experimental data.
Data Engineering & Platforms
Pipelines, integration and warehousing, including wiring existing informatics systems into a modern stack.
Four habits, run as a loop rather than a checklist.
They are also, conveniently, where our initials come from.
Aspire
Start from what the system should be able to do, not from what the current setup makes easy.
Innovate
Build from the ground up where it matters. Reuse ruthlessly where it doesn't.
Measure
Every claim we make about a system is a number we took ourselves, on your setup, and can take again in front of you.
Learn
Feed what the measurements say back into the next iteration, and keep going until the budget closes.
Aspire · Innovate · Measure · Learn, and back to Aspire
Six kinds of team, at both ends of the continuum.
Product OEMs
A feature has to ship on the part you already designed in. We make it fit rather than telling you to move up a tier.
Semiconductor vendors
Your silicon does more than your customers believe. We build the reference work that proves it.
Industrial & manufacturing
Inspection, yield, predictive maintenance and plant data, from the machine to the plant-wide view.
Life sciences & R&D
Complex, structured experimental data that never quite reaches a model. We connect it first, then model it properly.
Enterprise data teams
Pipelines, warehousing and integration for organisations whose data outgrew the systems holding it.
Software product teams
AI features inside an existing product: retrieval, agents and models that have to behave in production.
Notes from the team
Written by the engineers doing the work, across all six practices, not just the loud one.
-
Beyond Pixels: What If You Could Sense Someone Inside a Zone?
How millimetre-wave radar can tell whether someone is inside a defined area — without a camera, without light, and without creating a conventional image.
By Swathi Kalyan -
Accuracy Isn’t Everything: What Deploying AI at the Edge Taught Me
For a long time, I thought one number was enough to evaluate a model. Then I had to run one on real hardware, in real time, and realized that accuracy alone doesn't tell the whole story.
By Roshini Banu -
Data Engineering in AI: 80% Nobody Talks About
The AI revolution isn’t really about AI—it’s about data engineering wearing a shiny new suit.
By Manoj Gunasekaran
