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Models that fit the edge
Proprietary quantisation that fits vision language models onto constrained hardware without losing accuracy.
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Download againMost of this industry assembles cameras from parts. We research the parts. The vision language models, the quantisation that fits them onto a camera, the inference engine that runs them, and the optics that feed them are all our own work, because reasoning at the edge is a systems problem and cannot be bought in pieces.
Most vision language models need a data centre. We spent seven years teaching ours to reason on a camera at the roadside in real time.
Proprietary quantisation that fits vision language models onto constrained hardware without losing accuracy.
C++ inference that runs nine models at 60 FPS and answers natural-language queries in under 500 ms.
Global shutter imaging that holds a readable plate at 230 km/h through glare, rain, fog and night.
Models tuned on 1,000+ live deployments: Indian plates, traffic density and weather.
A world where every critical event on every road and across every city is detected, interpreted and resolved in real time.
Traffic, enforcement, and city-scale public safety still lose critical minutes between what happens, what it means, and what gets done.
Events go undetected when cameras only record. That is a failure of detection.
Footage piles up without meaning when no system can say what mattered. That is a failure of interpretation.
Even clear understanding fails if operators cannot act with speed and confidence. That is a failure of resolution.
We exist to close that loop in real time, on roads and across cities.
To build intelligent vision systems that help road and city operators see events, understand them instantly and act with speed and confidence.
We put understanding where the event happens, at the edge, and connect it to the command layer that turns many inferences into one clear picture an operator can act on.
Our focus is traffic, enforcement, and city-scale public safety. Our proving ground is India. Our market is the world.
A wrong-way vehicle, a crowd surge, a perimeter breach: these are decided in milliseconds. Edge intelligence is not a constraint we accommodate. It is our architecture.
The purpose of a camera has never been to produce storage. It has always been to produce answers, clear enough that an operator can act without scrubbing hours of video.
Hardware, models, and the command layer only work when they are engineered together. Reasoning cannot be assembled from unrelated parts.
We publish accuracy across day, night, rain, fog, glare, and density. Field evidence, not lab claims, governs our roadmap.
Quality is how quickly an event becomes an outcome: a complete evidence package, in plain language, that closes the loop.
These are not aspirational statements. They are the standards by which we make decisions and hold one another accountable.
We put resources only on work that advances the mission, and decline distractions that dilute clarity.
We favour disciplined, incremental progress. Sustained small gains compound into capability that cannot be purchased or copied.
We validate assumptions in live environments. Field results, not opinions, set the roadmap.
We take responsibility for outcomes end to end, and finish what we start without waiting for escalation.
We communicate clearly and keep our word. Reliability is built in small, consistent actions.
Our systems run on national highways, multi-lane free flow tolling and city corridors, including the 104 km Lucknow Ring Road. Roughly 40% of India's toll lanes and more than 5,000 lanes in total are powered by our computer vision work.
India's first vision language model running entirely on edge hardware was deployed by us, and demonstrated at the India AI Impact Summit.