Camera and sensor infrastructure is already live across our partner network.
Or write to raghav@voltq.com
VoltQ turns live camera and sensor infrastructure across 4,000+ real-world sites, run through our CCTV video analytics and wireless sensor partners, into structured training data for embodied AI, Physical AI and robotics teams building for the physical world.
VoltQ builds access to real-world environments, people, factories and retail spaces to help Physical AI and robotics companies develop the next generation of intelligent machines.
From egocentric video, SLAM data and human activity to spatial, multimodal and industrial data, VoltQ helps robotics teams source the data required to train, evaluate and improve embodied AI systems.
Robots trained on third-person footage learn to recognize actions from the outside. They miss the hand closing around a tool, the exact contact point, where the eyes moved half a second before the grip: the signals that separate a model that recognizes a task from one that can perform it.
That is why egocentric data, captured from the first-person view of someone actually doing the work, has become a default input for training Physical AI. And why spatial data from real, dynamic, GPS-denied environments is just as scarce for the navigation side of the same problem.
First-person task video
First-person task video from real factory and retail workers: hand-object interaction, tool use, assembly, stocking, packing and customer-facing tasks. Captured where the work actually happens, not staged for a camera.
Multi-sensor indoor data
Multi-sensor data from real indoor commercial environments: dynamic, cluttered, GPS-denied spaces that mirror exactly where mobile and service robots will need to operate. Camera and wireless-positioning data (Wi-Fi people counting) today, with IMU and depth capture on our roadmap.
Most egocentric and SLAM data providers start by building capture infrastructure from scratch: hardware, site access, a field workforce. We start from a different position. Through our CCTV video analytics and wireless sensor analytics partners, camera-based analytics already run across 4,000+ retail stores, wireless people-counting sensors are live in 500 of those stores, and an operations network is built for running physical rollouts at scale.
India's factory and retail floors are also some of the most varied working environments anywhere: different layouts, tasks, lighting and demographics than the home- and lab-collected datasets most Physical AI teams train on today.
You validate a small sample before we scale.
You tell us the task, environment and format you need.
We collect and deliver a small sample against your spec.
Once validated, we scale collection across our site network.
Structured delivery with documentation and an ongoing feedback loop.
Every site we collect from operates under a clear worker and customer consent process, with PII redaction built into the capture pipeline and full audit trails on request.
Data collection at this scale only works if it is done right. That is a floor for us, not a feature. Read our code of conduct.
IIT, Rice University and KIT research background, with long service in publicly listed organizations.
Computer vision & sensor deployments
Has taken Raspberry Pi computer-vision and wireless sensors from prototype to production in retail stores and garment factories, including RFID for lean manufacturing. IIT BHU Varanasi (B.Tech) and Rice University (M.S.).
Finance
Spent 39 years with ITC Ltd in the finance function and brings that financial discipline to VoltQ.
Logos show team members' education and employment history. They are not partnerships or endorsements.
Tell us the task, environment and format you need. We will come back with a pilot plan for a small sample dataset.
