JioVidyut.ai
One platform that turns your data into clear answers and action. Every step stays inside a secured network.
Sector-specific answers, lower running costs, and full control of memory.
JioVidyut.ai is an agentic AI system for the power sector. Teams ask questions in plain language and get answers based on the organization's own data. Every answer also draws on a power-sector knowledge base, current sector news, public regulations, and the organization's private documents. AI agents then carry each answer through to action, from a PDF report to a task in the field team's tracker.
The system works with any AI model, and self-hosted models running inside a secured network keep costs low. The organization controls what the shared memory holds and can add its own machine learning models and dashboards. New insights build on earlier ones as the system runs.
What makes JioVidyut.ai different?
Recovered revenue
Theft and metering gaps appear as a ranked list with the field action attached, so recovery starts with the strongest cases.
Lower power purchase and deviation cost
Forecasts for demand and solar output show what drives them. Schedules hold at the load dispatch centre, and deviation charges fall.
Fewer unplanned outages
Health scores point engineers to a weak battery unit or solar inverter before a failure.
Faster answers for every team
Staff get answers to data and policy questions in minutes, with no wait for an analyst. Reports arrive on a set schedule.
Low risk for IT and security teams
The system runs inside a secured network and masks personal details. Every action passes a policy check, and an append-only log keeps a record of each decision.
Answers that improve with use
The system learns from each correction and retrains its models on user feedback, so each fix carries into later answers.
Features
Plain-language questions in many languages
Ask in any language and get charts, PDFs or Excel lists.
Answers from documents and sector news
Answers on policy, tariff orders and sector news, with sources cited.
Actions inside existing tools
AI agents write files, raise tasks and send emails in your tools.
Plug-and-play integration
One API connects your systems, with no data migration or coding.
Guardrailed execution and access control
Every step is checked, role-controlled and recorded in an audit trail.
Self-learning
Learns from feedback, retrains its models and flags key changes.
Use cases
Energy theft detection for DISCOMs
JioVidyut.ai checks smart meter events against billing records and ranks the connections most likely to involve theft.
The system reads meter events such as tamper and reverse flow, and matches them with interval readings and billing records for each connection. The detection model scores every connection against its own history and against similar connections. JioVidyut.ai ranks the suspects and explains the reason for each flag. The output includes a summary with detailed insights and an Excel list of flagged users.
Revenue recovery starts with the strongest cases. Each case comes with a recommended action and a task for a named owner in the field team. The report reaches the right inboxes on a set schedule. Field feedback on each case retrains the model, so accuracy improves with use.
How it works
Scores every connection and gives the reason for each flag
Lists flagged users in a ranked Excel file
Raises tasks in the utility's own system with a named owner
Emails the report on a set schedule
Retrains the model from field feedback
Energy demand forecasting
JioVidyut.ai turns past consumption into a view of future demand that planners can use for power purchase.
The forecasting model learns from past consumption data with consumer category and geography tags. Planners can split the forecast by consumer category and by geography, from the whole utility to a single feeder. History and forecast appear on one graph. The insight names the drivers behind the shape of the curve.
Better forecasts lead to cheaper power purchase and tighter capacity planning. The schedule holds at the load dispatch centre, which lowers deviation charges, and the forecast refreshes at intervals that the utility sets.
How it works
Forecasts demand for the whole utility or for any split to feeder level
Shows history and forecast on one graph
Names the drivers behind each change
Refreshes the forecast at set intervals
Shares the schedule with the dispatch centre
Asset health score for battery storage (BESS)
JioVidyut.ai rolls battery data into one health score per installation and names the units to inspect.
The system reads cell voltage spread, cell temperature, state-of-charge cycles and round-trip efficiency from each battery energy storage system (BESS). The model combines these signals into one score per installation. The score shows how many points each driver takes away. The output includes a custom HTML report with charts and recommended actions, along with a PDF summary.
Engineers reach a weak unit before the unit fails, which cuts unplanned outages. Each flagged unit becomes a task in the engineering team's queue, and the driver breakdown lets engineers check the reasoning before they act.
How it works
Gives one health score per installation
Shows the points lost to each driver
Builds a templated HTML report per installation and a PDF summary
Assigns named units to engineering as inspection tasks
Solar generation forecasting and plant performance
JioVidyut.ai forecasts the output of each solar plant and flags the inverters and strings that produce less than expected.
The system reads data from the plant's SCADA or monitoring system. This covers generation meter data, inverter and string output, irradiance, module temperature and weather forecasts. A forecasting model projects output in 15-minute blocks for the day ahead and within the day. A second model compares actual output with the output expected for the sunlight received. When the two differ, the model names the likely cause, such as soiling, shading, an inverter trip or a string fault. For DISCOMs, the same models can estimate rooftop solar output across the service area and feed the demand forecast.
A forecast close to actual output keeps deviation charges low. Operations and maintenance (O&M) teams get a task for each flagged inverter or string, with the likely cause attached. Crews start with the equipment that loses the most generation. Plant managers track performance ratio and availability without building reports by hand. DISCOMs plan daytime demand with rooftop solar output in view.
How it works
Forecasts generation day ahead and within the day in 15-minute blocks
Compares expected and actual output for each inverter and string
Names the likely cause of each loss, such as soiling or an inverter trip
Scores plant health from performance ratio, capacity utilisation factor (CUF), availability and inverter efficiency
Raises cleaning and repair tasks for the O&M team with an owner
Answers questions on solar policy, such as net metering rules and deviation settlement regulations
Industry
Why JioThings
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A strong team with a unique blend of technology and business verticals.