About Smart Bhujal
Smart Bhujal came out of IIT Tirupati with one conviction: most water decisions are still made without a model. Not because the science is missing — groundwater and floods have been simulated for decades — but because the distance between a calibrated model and the person who has to act on Monday morning is enormous.
We close that distance. We build the digital water layer between raw data and a real decision, and we build every layer of it ourselves — from the satellite pixel to the dashboard your team opens each morning.
Who builds it
Water modelling and machine learning are rarely found in the same room, which is why so much water software is one done well and the other done badly. Smart Bhujal was founded by people who hold a doctorate in each.
Founder & CEO
Doctorate in hydraulics and water resources from IIT Tirupati, with senior consulting work for the International Water Management Institute. Ten years across water engineering, software and AI — the combination the company is built on. Leads how a water question becomes a system that answers it.
CTO & Co-founder
Doctorate from IIT Tirupati and a machine-learning and edge-AI specialist, previously at Qualcomm. Builds the modelling and AI layer: forecasting, anomaly detection, classification and the deep-learning work that turns raw sensing into a usable signal.
Advisor — AI / ML
Associate Professor of AI and machine learning at IIT Tirupati and a former researcher at Harvard. Advises on model architecture and on keeping the methods defensible rather than merely impressive.
Advisor — Digital Water
Former IIT professor with more than twenty years in digital water and a research background at Michigan State. Advises on numerical modelling practice and on what actually survives contact with a real basin.
How we work
We ask what you are trying to decide before we ask what data you have. A dashboard nobody acts on is a cost, not an asset — the decision determines the model, and the model determines the data, in that order.
Groundwater and floods obey equations that have been calibrated against field data for decades. We use MODFLOW, HEC-RAS, EPANET and SWMM where physics is the right tool, and machine learning where it genuinely beats them — forecasting, anomaly detection, downscaling, filling gaps.
A model that has not been checked against measurements is an opinion with a mesh. Every system we build is anchored to ground truth and recalibrated as new observations arrive, which is why monitoring is part of the stack rather than an afterthought.
Incubated & backed by
We are incubated at IIT Tirupati's Technology Business Incubator, with access to its research facilities and faculty, and supported by global technology programmes.
Who we work with
Our work runs from international research programmes to state rural livelihoods missions and instrument makers — which is deliberate. A method that only holds up in a paper is not much use to a district officer.
Given away
Groundwater data belongs to the people standing on top of it. Two of our tools are open to anyone, with no account and no cost.
Start with the decision
Describe the water question in front of you and we'll tell you honestly whether a decision-support system is the right answer — and what it would take to build it.