Vehicle-to-vehicle intelligence · Built in India
Let Vehicles Talk.
Building the world model for vehicles: a shared map of every road, built from what vehicles see and share.
V2X and V2V communication, ADAS data training and edge AI for autonomous vehicles, from Indore, India.
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From orbit to the street
Autonomous vehicles still sense alone.
Robotaxis and self-driving vehicles are already sharing roads with people. The next step is for them to share what they know.
Vehicle-to-vehicle
Vehicles coordinate before they move.
A pedestrian steps toward the crossing. Every vehicle in range knows at once and yields together. No horn, no guess, no one braking late.
- Warn A hard brake ahead reaches every vehicle behind it.
- Yield What one vehicle sees, all of them know.
- Agree Merges and gaps settled in advance, not at the last second.
Illustrative simulation of cooperative driving. Not a product demonstration.
Products
What BharatNex builds.
Autonomy Training System
The end-to-end ADAS data training system for autonomous vehicles: from raw sensor data, through our ALICE encoder, to training, validation and back out to the fleet.
Explore the training system →
V2X & V2V
The vehicle-to-vehicle message and decision layer: a hard brake ahead reaches the vehicles behind before their sensors can see it. Designed for India's AIS-230 path.
Explore V2X & V2V →
ADAS Training & Workshops
ADAS training and workshop for future engineers, hands-on with a real rover and its Jetson Orin, and a campus programme for colleges and clubs.
Explore the ADAS workshop →
About BharatNex
One world model, shared by every vehicle.
BharatNex is a deep-tech company in Indore, India, building V2X and V2V communication, an ADAS data training system and edge AI for autonomous vehicles. We are solving the data pipeline for autonomous systems, and V2X for India's roads, with our own V2X technology. Our direction: a map of the world that autonomous vehicles build together, so they can ease congestion and drive themselves safely, everywhere.
Funded and supported by
About the team
The founding team.
BharatNex is led by its four co-founders across engineering, product and marketing. Select a photo to open their LinkedIn profile.
Achievements
Recognition and milestones.
NASSCOM
Mobility Innovation Challenge — finalist.
Baja SAE India
Our founders' roots: student teams building autonomous (aBaja) vehicles.
Maruti Suzuki Nurture
Cohort 4 — shortlisted for the regional pitch, Delhi NCR (September 2026).
FAQ
Questions about BharatNex.
What is BharatNex?
BharatNex (BharatNex Energy Private Limited) is a deep-tech startup in Indore, India, building technology for autonomous and connected vehicles. It makes BNEX V2X, a vehicle-to-vehicle (V2V) communication module, and an ADAS data training system built around ALICE, an edge AI multimodal encoder. It also runs hands-on ADAS training workshops for engineers. BharatNex was founded in 2024 and is a DPIIT-recognised startup.
What problem is BharatNex solving for autonomous vehicles?
Two linked problems. Autonomous vehicles still sense alone: each relies on its own sensors, so a hazard hidden from one vehicle stays hidden. V2X lets vehicles share what they are about to do. And training ADAS and autonomous-driving models needs large volumes of sensor data that is slow to collect, move and label, so BharatNex's training system understands and encodes that data on the vehicle. The direction is a shared world model that every vehicle can contribute to.
What is V2X, and what is V2V?
V2X (vehicle-to-everything) is communication between a vehicle and everything around it: other vehicles (V2V), roadside infrastructure such as traffic signals (V2I), pedestrians (V2P) and the network or cloud (V2N). V2V is the vehicle-to-vehicle part, where vehicles broadcast their state and intent, such as a hard brake, directly to the vehicles near them. BharatNex builds the BNEX V2X module for Indian roads. Read about V2X and V2V.
What is an ADAS data training system?
It is the pipeline that turns raw vehicle sensor data into a trained ADAS or autonomous-driving model: processing, labelling, quality checks, training, simulation and validation, then deployment back to the fleet. BharatNex runs it on the vehicle's own edge computer, with ALICE as a read-only sidecar that observes the sensor streams and never controls the vehicle. Read about the training system.
What is edge AI in a vehicle?
Edge AI means the AI model runs on the vehicle's own computer instead of in a remote data centre. That reduces the data that has to be uploaded and shortens the time between something happening on the road and the system learning from it. ALICE, BharatNex's model, runs at the edge, inside the vehicle.
What is a multimodal encoder?
A multimodal encoder turns several kinds of sensor data, such as camera and radar, into one compact numerical representation, called a latent record, that a perception model can learn from. Unlike a video or multimedia codec, which compresses pixels for people to watch, ALICE keeps what a perception model learns from and leaves the redundant bulk of the raw footage behind.
Who founded BharatNex, and where is it based?
BharatNex is based in Indore, Madhya Pradesh, India, and was founded in 2024. The four co-founders are Anish Moolchandani (CEO), Rajeev Parmar (CTO), Dhananjay Sharma (CPO) and Rinky Moolchandani (CMO).
How can I work with BharatNex?
OEMs, Tier-1 suppliers and autonomous-vehicle teams can ask for a V2X pilot or a demonstration of the ADAS data training system. Colleges, clubs and students can request the ADAS training workshop. Use the contact form on this page or write to [email protected]. We reply within a working day.
Contact us
Let vehicles talk. Let's talk first.
Tell us which programme you are planning for. We reply within a working day, across time zones.
Open the enquiry form
For anything else, write to [email protected].
Indore, Madhya Pradesh, India · Founded by Anish Moolchandani
BharatNex™ · Founded in 2024 · DPIIT-recognised startup · Udyam-registered · Patent application filed.
© 2024–2026 BharatNex Energy Private Limited. All rights reserved.