Auralis
On-device seizure-risk model for a wearable EEG band. We compressed a research network to 4.8 MB and kept clinical sensitivity above 94% in overnight trials.
- Edge inference
- Biomedical signal
- FDA-ready logs
Applied intelligence studio · est. 2021
Systems that think
in the world,
not just on the page.
NeuronicsLab builds intelligent products where neural science, machine learning, and hardware meet. We design models you can ship, sensors you can trust, and interfaces that feel inevitable.
- Research to product
- 48 live systems
- Latency on the edge
- < 12 ms
- Labs & partners
- 11 cities
The lab
A studio that treats intelligence as a material.
We started NeuronicsLab because most “AI products” stop at a demo. The interesting work happens later — when a model has to hold a signal in noise, run on a battery, and stay accountable to a person.
Our team pairs research scientists with industrial designers and firmware engineers. That mix lets us move from paper to prototype without handing the problem to three different vendors.
- 01 Models shaped by the physics of the sensor, not the other way around.
- 02 Interfaces that explain uncertainty instead of hiding it.
- 03 Systems designed to be audited, updated, and retired cleanly.
Selected work
Recent systems in the field.
A sample of engagements across health, mobility, and industrial sensing. Names of confidential partners are withheld.
Lumen Path
Perception stack for low-light last-mile robots. Fused event cameras with a compact transformer so the fleet keeps moving when streetlights fail.
- Event vision
- Sensor fusion
- Fleet telemetry
Helix Line
Acoustic anomaly system for high-speed packaging lines. Operators get a spatial heatmap, not a binary alarm, so maintenance happens before the stoppage.
- Acoustic ML
- Human review
- Plant integration
Capabilities
What we actually build.
Neural interfaces
Biosignal pipelines, artifact rejection, and models that stay stable on skin, sweat, and motion — not just in the lab.
On-device intelligence
Quantization, distillation, and custom kernels so a model can live on a microcontroller without begging the cloud.
Perception systems
Vision, audio, and multimodal stacks for robots, vehicles, and instruments that have to decide in milliseconds.
Research translation
We take a paper, a dataset, or a half-working prototype and turn it into a product with tests, docs, and an owner.
How we work
Four movements. No theatre.
-
01
Listen to the signal
We start with the sensor, the user, and the failure mode — not a model card.
-
02
Prototype in the wild
Field kits go out early. Lab accuracy that dies on a factory floor is not a result.
-
03
Harden the system
Latency budgets, calibration, fallbacks, and the boring work that keeps people safe.
-
04
Hand over ownership
Your team leaves with the model, the data contract, and a way to keep improving it.
Lab notes
From the bench.
Why event cameras finally matter for night robotics
A short field report from the Lumen Path deployments in monsoon lighting.
Compressing EEG models without losing the rare events
Distillation tricks that keep minority-class recall intact on-device.
Uncertainty is a feature, not a disclaimer
How we design operator UIs that show what the model does not know.
Start a conversation
Tell us what the system has to do in the real world.
We take on a small number of briefs each quarter — products, research translations, and long-term lab partnerships.
hello@neuronicslab.com