Discoms fight AT&C losses and collection gaps, renewables reshape the daily schedule, power moves onto exchanges in fifteen-minute blocks, pipelines and plants must prove their integrity, and regulators ask how every tariff, emission and safety number was reached. The answer runs across the whole value chain — from how power is generated and delivered to how a consumer is billed and the ledger closes. DaasLabs is the data and AI services team that helps utilities, oil and gas companies and resource businesses make that shift, one domain at a time.
Energy companies organised around assets — a plant, a grid, a pipeline, a billing system — now compete on decisions made across all of them. The ones pulling ahead treat data as a product and AI as an operating capability, not a set of pilots. In India, these are the pressures they are responding to.
Technical losses, theft, faulty meters, unbilled energy and slow collections erode revenue before and after it is invoiced. Loss-reduction targets are now tied to the funding discoms depend on.
Data & AI: feeder- and DT-level energy accounting, theft detection, inspection targeting and collection analytics.
Solar and wind make every fifteen-minute block harder to forecast. Forecast errors become deviation charges, and thermal fleets are asked to flex harder than they were built to.
Data & AI: plant-level generation forecasting and scheduling options for the desk.
Day-ahead, real-time and term-ahead markets, green segments, RECs, derivatives and market coupling mean prices and positions change every fifteen minutes — and solar hours can push real-time prices close to zero.
Data & AI: price and load forecasts, bid preparation, DSM watch and settlement reconciliation for the trading desk.
See the market dataPipelines, gas networks, plants and substations carry safety-critical risk, yet surveys and inspections are often captured on paper and designs checked by hand.
Data & AI: offline field capture with anomalies flagged at source, and AI review of piping drawings.
Steel, cement and power face carbon-intensity scrutiny from buyers, lenders and regulators. Energy per tonne and emissions per unit have to be measured, not estimated.
Data & AI: plant-level energy and emissions tracking with lineage to the meter and the furnace.
Tariff petitions and true-ups, grid-code compliance, reform-scheme milestones and the Digital Personal Data Protection Act all ask for traceable data, delivered faster.
Data & AI: lineage behind every filing and consumer record, and AI-assisted regulatory data packs.
SCADA, meter data, billing, GIS, plant historians and ERP were bought separately and speak different languages. AI that acts on this has to be governed, explainable and safe.
Data & AI: a data product factory and marketplace across OT and IT, and agents with autonomy limits and audit trails.
The response isn't one platform or one model — it is data and AI applied domain by domain, from the plant to the meter, on a shared, governed foundation.
See where, domain by domain ↓The power chain from generation and the grid to the consumer and the trading desk, oil, gas and heavy industry, the assets they all depend on, and the group functions underneath. Select a domain to see the data it runs on, the AI opportunities, and how DaasLabs adds value there.
Solar and wind now swing the daily schedule. Every fifteen-minute block has to be forecast and scheduled to the SLDC or RLDC, forecast errors turn into deviation charges, and thermal units are asked to ramp harder than they were designed for.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
More renewables, more inter-state flows and new storage make the grid harder to balance. Outages, line loading and protection events are logged in separate systems, and asset condition is known only when something trips.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Aggregate technical and commercial (AT&C) losses decide whether a discom survives. Theft, faulty meters, estimated bills and slow collections hide in millions of consumer records — and smart prepaid metering under RDSS creates the data to fix them, if feeder, meter, billing and payment systems can be joined.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Power is now bought and sold in fifteen-minute blocks on the Day-Ahead, Real-Time and Term-Ahead markets, in green segments and RECs, bilaterally and through derivatives — while market coupling, deviation-settlement reform and solar-hour price collapses change the rules. A trading desk lives or dies by how fast it can trust its forecasts and positions.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
From upstream fields to refineries, cross-country pipelines, city gas distribution and retail fuel outlets, integrity and safety data is captured in the field, often on paper, and pipeline designs are checked by hand.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Steel, cement, aluminium and mining run on furnaces, kilns and fleets where energy is the biggest cost and emissions the biggest scrutiny. Plant data lives in historians and spreadsheets, a long way from the margin it decides.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Transformers, turbines, compressors and pipelines are inspected on calendars rather than condition. Maintenance history is free text, spares are stocked for the worst case, and safety-critical findings wait in inboxes.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Tariff petitions, true-ups and regulatory filings to CERC and SERCs ask for numbers that reconcile to the meter. Energy cost decides margins at every site, and ESG and BRSR disclosures add another set of figures to defend.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Opportunities and approaches are described qualitatively. Shaded chips are DaasLabs service lines; the others open accelerators. See every service line mapped to these eight domains
Market data, not DaasLabs results. India's short-term power market keeps growing, the Real-Time Market is its fastest-growing segment, and the rules are changing under traders' feet.
| Day-Ahead Market (DAM) | Next-day delivery in 15-minute blocks; the price reference |
|---|---|
| Real-Time Market (RTM) | Gate closure one hour ahead; fastest-growing segment in 2025-26 |
| Term-Ahead Market (TAM) | Contracts for delivery up to 90 days ahead |
| Green DAM / TAM & RECs | Renewable power and certificates for RPO and green buyers |
| Bilateral & VPPA | Direct contracts; VPPA guidelines from December 2025 |
| Deviation settlement (DSM) | The price of being off schedule |
| Derivatives | Monthly baseload contracts on MCX and NSE since July 2025 |
So what for a trading desk: forecasts, positions and DSM exposure have to be right by gate closure, every block — which makes trusted, on-time data products the deciding capability.
Market data from Indian Infrastructure, July 2026 (FY2025-26 volumes, regulation) and Power Line, October 2025 (FY2024-25 RTM prices, RECs). Not DaasLabs results.
Trading desks, discom operations, generation, finance and regulatory teams all need the same forecasts, schedules, meter data and market results — on time, trusted and governed. We build them once as data products in a factory, and publish them in a marketplace where every team can discover, request and subscribe to them.
Cloud-agnostic: the same pattern runs on Azure (for example Microsoft Purview and Microsoft Fabric) or on-premise.
Illustrative catalog entries — owners, freshness and consumers are agreed with you; “Subscribe” is not live here.
Built on the Data Fabric Framework's 4C method. The framework →
Amazon SageMaker Catalog, built on Amazon DataZone, lets producers group assets into “well-defined, self-contained packages” called data products, publish them with business metadata, glossary terms and metadata-enforcement rules, and route consumer subscriptions through approval, with access granted through AWS Lake Formation or Amazon Redshift. Publish · Subscribe
AWS Data Exchange is the external marketplace — 3,500+ data products from 300+ providers, delivered as files to Amazon S3, as Amazon Redshift tables or through APIs — to buy weather, fuel or carbon data, or to share your own products with partners. Source
Data mesh: producer domains — generation, grid, trading, discom — own their products; consumer domains subscribe through one central catalog, with permissions managed in AWS Lake Formation. AWS guidance
Nine service lines that advise, build, transform and run — delivered on three pieces of DaasLabs IP, so utilities, oil and gas and resource companies start from working components rather than a blank page.
In a business where a wrong action can black out a feeder or breach a pipeline, agents observe, flag and draft; the arithmetic runs in SQL; a named engineer or officer decides. Nothing happens off the record.
The agent compares feeder input with billed energy, finds the distribution transformers and meters behind the gap, ranks premises for inspection and drafts the list. The vigilance officer decides who is inspected.
See the energy agent squadsScanners run over SCADA, meter, billing, GIS and plant data on the governed Data Fabric and surface signals with hard numbers.
Each agent has an autonomy level. Anything touching grid switching, plant control or pipeline safety stays at observe or suggest.
Every finding carries the evidence, a rupee impact, a named owner and an SLA — accept, assign, escalate or dismiss.
Every plan, tool call, guardrail check and decision is logged; overrides feed evaluation, and a kill switch halts all agents.
Platforms we have delivered and built for oil and gas assets, site energy, pipeline integrity, piping design, ESG and heavy industry, generalised into configurable starting points — plus cross-industry accelerators our teams configure to utility data, rules and controls.
Delivered for oil and gas, Indian infrastructure, pipeline-services and heavy-industry clients; each runs on the Data Fabric Framework.
A multilingual LLM platform over the maintenance system and hundreds of thousands of technical documents. Field engineers ask by voice, in their own language and in noisy plants, and get answers with on-demand visualisations.
Joins electricity billing with operational run-hours by source — grid, diesel generator, battery and solar — into a per-site energy passport, a composite Energy-360 score, a what-if stress lab and solarisation and anomaly opportunities.
A mobile app for cathodic-protection surveys that works with no signal: reads a digital voltmeter over Bluetooth, stamps every reading with GPS and its accuracy, flags under-protected sections at the point of capture and syncs in batches with retry.
Cross-industry accelerators below are configured to utility data in an engagement; their demo pages run on banking sample data.
Delivered work and platforms we have built. Client names are withheld. The first two figures are delivered results; the others are counts from the builds, not client results. Read the case study.
A multilingual LLM platform integrated with the maintenance system and 500,000+ technical documents, answering field engineers by voice in Arabic or English.
A month of electricity billing (over a million rows) reconciled with operational run-hours by source into a per-site passport, an Energy-360 score, a diesel, tariff and solar stress lab and an AI analyst.
Readings from a Bluetooth voltmeter with GPS and accuracy, anomalies flagged against the protection criterion at capture, CSV export and batch sync with acknowledgement.
AI vision reads isometrics, flags design errors, explains them in a chat and generates corrected schematics.
Executive, production, sales, supply-chain, quality and sustainability analytics across a multi-plant network, with ML insights and an AI chat.
Delivered asset intelligence for a national oil company in the Gulf, and platforms we have built for a national telecom-tower company in India managing energy across its sites, a pipeline-integrity services firm, heavy industry and an Indian cement maker preparing its ESG disclosures. Different assets, the same pattern: reconcile the raw feeds, model them once, and let AI explain what it finds.
The first two figures are delivered results for the Gulf oil company; the others are counts from the platforms as built. Where demonstration data is modelled or synthetic, we show capabilities rather than client results.
The challenge
Critical asset knowledge was locked away from the field.
What we built
Voice-first, bilingual asset intelligence.
The challenge
Energy was the biggest cost, seen one bill at a time.
What we built
A per-site energy passport and a live stress lab.
The challenge
Survey data was cleaned after the fact, far from the pipe.
What we built
An offline-first app that gets the data right at source.
The challenge
ESG obligations multiplied faster than the team.
What we built
An ontology- and graph-driven ESG reasoning engine.
| Component | Weight | What it rewards |
|---|---|---|
| Cost efficiency | 30% | Lower rupees per kWh delivered |
| Reliability | 25% | Grid availability, fewer diesel hours |
| Sustainability | 30% | Solar share, lower CO2 per kWh |
| Data quality | 15% | Billing and operations that agree |
Weights are configurable. Emission factors and diesel-generator yield are set in the model, not hard-coded in the score.
Heavy-Industry Plant Cockpit — for a global refractories maker serving steel, cement and metals: executive, production, quality, supply-chain and sustainability views, OEE and kiln utilisation, CO2 by plant, ML insights and an AI chat.
Enterprise cockpits for Indian power and agri-energy groups — a power transmission and distribution EPC and a sugar, ethanol and co-generation group: order book, projects, customer and cash 360s, a control tower and AI briefings, across 46 cockpit pages.
Isometric Drawing AI — AI vision that reads piping isometric drawings, flags design errors, explains them in a chat and generates corrected schematics.
Strategic Value Cockpit — for a flow-control equipment maker serving energy markets: strategic objectives mapped to quantified impact and the data and AI capabilities behind them.
Technology
Enter your own volumes. The estimate compares today's manual handling with agents working the cases and people reviewing only the exceptions.
Estimate only, not a quote or a DaasLabs result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour (× 12 for a year), shown in lakh (L) and crore (Cr). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Tell us the problem — a feeder that loses energy, collections that lag, bids that miss gate closure, deviation charges, survey data on paper, a plant whose energy per tonne won't move, a platform to modernise. We'll propose an assessment or a 30-45 day pilot, delivered by DaasLabs teams on our framework and accelerators.