Data & AI services for energy companies — advise, build, transform, run.
SCIKIQ teams set your data and AI strategy, build the platforms, transform trading, distribution, generation and assets, and run what we build — for discoms, generators and grid operators, oil and gas companies, and mining and metals businesses. Our SCIKIQ Data Fabric, pre-built accelerators and a supervised digital workforce of AI agents are how we deliver it faster.
Energy data and AI programmes stall because every one starts from zero.
OT and IT systems that never agree, asset knowledge held as free text and long implementations mean value arrives late — or not at all.
Patterns we found in delivered energy and heavy-industry work — not industry statistics.
1 · No governed data foundation
SCADA, meter data, billing, GIS and ERP don't agree — sites and assets are counted differently and lineage is unknown.
2 · Services firms rebuild from scratch
Each project re-invents pipelines, models and controls, so timelines and fees grow.
3 · Software alone doesn't change the process
Point products solve one use case and leave integration, data and adoption to the utility.
4 · AI pilots never reach production
Proofs of concept built without data, controls and an operating model stay on the shelf.
Energy companies are moving from dashboards and copilots to agents that watch, explain and draft — with people deciding.
Agents now watch feeders, meters, plants and pipelines, explain what changed and draft the next step; engineers and officers decide.
So what for an energy company: the operating model moves from “a person reads the reports” to “agents do the work, a person supervises many agents” — which makes validation, autonomy limits and audit trails the deciding capabilities, especially where grid, plant and pipeline safety are involved.
A platform that arrives with industry IP and the team to run it — so energy companies pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know power distribution, generation, oil and gas, and heavy industry.
SCIKIQ Data Fabric
The governed foundation: the 4C method, 268 data sources, metadata, lineage, data quality and security.
Accelerators
The Asset Intelligence Assistant, Energy 360 for Distributed Sites, the Field Integrity Collector and the Heavy-Industry Plant Cockpit — plus cross-industry accelerators for reconciliation, revenue assurance and FP&A.
Supervised digital workforce
AI agents that do the routine work end to end, with people approving exceptions and every step logged.
Nine service lines cover the lifecycle, organised the way energy companies buy them.
Select a stage on the wheel, or start from your role.
Set direction and the rules the data must meet.
Data & AI StrategyMaturity assessment, AI operating model, business case Data Governance & Regulatory DataData marketplace, filing lineage, ESG & BRSREngineer the platforms and the AI that runs on them.
Data Engineering & Platform ModernisationData product factory over SCADA, AMI, markets & ERP AI & Agentic EngineeringSupervised agents, multilingual & voice GenAIChange how an energy function works, end to end.
Power Trading & Market OperationsDAM / RTM bids, DSM, RECs, settlement Distribution, Metering & Revenue ProtectionAT&C losses, theft, billing & collection Generation, Renewables & GridForecasting, scheduling, grid events Oil, Gas & Asset IntegrityAsset intelligence, pipelines, plantsKeep it healthy and improving after go-live.
Managed ServicesDataOps, MLOps & AgentOps under SLAsEvery service line runs on the same framework, accelerators and digital workforce. All services in detail →
A repeatable 4C method turns raw energy data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect268 data sources: SCADA and historians, smart meters and billing, exchange results, schedules and DSM accounts, network GIS, maintenance systems and ERP, field surveys and documents; batch and streaming.
- Step 2 · Week 2–4CurateStandardise, cleanse and enrich: MDM, de-duplication and data quality rules.
- Step 3 · Week 3–5ContextualizeMetadata catalog, critical data elements, multi-hop lineage, ownership and policy.
- Step 4 · Week 4–6ConsumeData products, APIs, BI, NLQ GenAI studio and the agents behind each accelerator.
Twelve capability layers give every energy company one blueprint — and one way to measure progress.
The same layers we build and score in the maturity assessment. Hover a layer to see what it does.
Runs in your cloud tenancy or on-prem. Code, ontology and agents are handed over — you own what we build. Explore the blueprint →
MIT CISR found enterprises at stages 3–4 perform well above their industry average financially, while those at stages 1–2 perform below it. Source
Energy and cross-industry accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points from delivered work, on the framework. Hover or tap a tile.
Energy accelerators Ops · Asset · CFO
Trading, revenue & ESG Trading desk · CFO
Operations & automation Ops
Data Governance & Regulatory CDO
A data product factory and marketplace give every desk the same trusted data, on time.
Forecasts, market results, meter and asset data built once as governed data products — owner, contract, freshness SLA, lineage — and published in a catalog teams subscribe to.
Illustrative data products. Cloud-agnostic — Azure equivalents (Purview, Fabric) available. AWS mapping from AWS documentation: Amazon DataZone data products and subscriptions; AWS Data Exchange (3,500+ products from 300+ providers). Full section →
28 agent roles across 8 energy domains — your engineers and officers supervise the exceptions.
Agents work across generation, the grid, discoms, the trading desk, oil and gas, heavy industry, assets and finance. Policy decides what goes straight through; people approve the exceptions — and no agent switches, dispatches, controls a plant, submits a bid or approves a dig.
Deviation Analyst · L1
Plant Performance Watcher · L0
Trip Root-Cause Analyst · L1
Substation Health Scorer · L1
Theft & Tamper Detector · L1
Meter Data Validator · L2
Bill Dispute Triage · L2
Payment & Subsidy Reconciler · L3
Bid Preparation Agent · L2
DSM & Imbalance Watch · L0
Green & REC Strategist · L1
Settlement Reconciler · L3
CP Survey Anomaly Flagger · L1
Isometric Drawing Reviewer · L1
Energy & Emissions Tracker · L1
ESG Disclosure Assistant · L1
Work-Order Classifier · L2
Spares Optimiser · L1
Regulatory Pack Assembler · L2
Data Product Steward · L1
Click a bar to see the agents in it.
Agent designs from our AI & Agentic Engineering practice — not client results. The live control-room demo runs on banking sample data.
An agent does the legwork on every case; a person makes the call when policy says so.
Illustrative replay: a batch of UPI bill payments doesn't match the bills it should settle. The Payment & Subsidy Reconciler works it.
- 1Source systemsA day's UPI and bill-payment settlement file arrives; some payments can't be matched to a consumer's bill — a wrong account number, a partial payment, a duplicate.
- 2Data fabricThe settlement file, the billing ledger and the bank statement land within minutes, quality-checked, as governed data products.
- 3Metadata & ontologyPayments, bills and consumers resolve to the same consumer and connection on the master record, with lineage back to each source.
- 4ContextTolerance rules, past mismatches for the same channel and the revenue procedure are assembled — only what this officer may see, with consumer data masked.
- 5AgentPlans and calls tools through the secure AI gateway:
get_settlementfind_billsget_bank_credit; proposes a match, a split or a refund query with evidence and a confidence score. - 6PolicyKill switch, autonomy level, confidence ≥ 0.90 and a difference within tolerance decide: straight through, or to a person.
- 7Human gateThe accounts officer (revenue) reviews the evidence on one screen and approves, edits or rejects the proposal.
- 8ActionA governed, typed action posts the payment to the right bill — or drafts a refund or consumer query — limited, idempotent and reversible.
- 9AuditEvery step, tool call and decision is recorded; outcomes feed the evaluation that decides whether autonomy can be raised.
Autonomy is set per agent and kept low wherever safety or supply is at stake — inside hard guardrails.
Confidence threshold
Straight-through only if confidence, amount limit and evidence checks pass.
Amount limits
Bill corrections, refunds and settlement differences above the limit always go to a person; every exchange bid is submitted by a trader.
Named human owner
Approves, edits or rejects every exception; grid switching, plant control and pipeline-integrity decisions always stay with people.
Everything logged
Every plan, tool call and decision; overrides feed evaluation. Personal data masked in prompts.
Kill switch
One control halts every agent at once.
What could a supervised agent squad free up in your business?
Move the sliders to your own volumes. Agents work the cases; people review only the exceptions.
Estimate only, not a quote or a SCIKIQ 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, in lakh (L) and crore (Cr). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Faster than services alone, a better fit than software alone.
How the SCIKIQ model compares with the usual ways energy companies deliver data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | SCIKIQplatform + accelerators + delivery |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| Energy data models & domain depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | SCIKIQ Data Fabric |
| Ready-made accelerators | Varies | Single product | None | Energy + cross-industry |
| Supervised AI agents in operations | Pilots / PoCs | Copilot features | Build & govern | Agent squads with guardrails & AgentOps |
| Process change & adoption | Yes | Left to the utility | Partial | Yes |
| Run & continuous improvement | Separate contract | Product support | Internal team | Managed services |
| Total cost of ownership | High | Medium–High | Very High | Low |
Big-4 / SI (6–12 months), point products (3–6 months + integration) and in-house builds (12–18 months) are compared on larger screens.
Four phases, each ending with something you keep — and three ways to buy it.
Discovery & Planning
You receive a maturity assessment and target blueprint, with a prioritised roadmap.
Gate: pilot scope signed offAnalysis & Design
Target-state design and ontology, mapped to your controls; agent autonomy agreed with operations, safety and finance.
Gate: design authorityBuild & Deploy
Landing zone as code, pipelines, accelerators and agents configured and tested on real data.
Gate: go-live readinessSupport & Embed
Runbooks, evaluation suites and knowledge transfer to a trained team.
Gate: handover sign-offStaff Augmentation
Architects, data and AI engineers embedded in your teams, under your delivery lead.
Project Delivery
Outcome-based delivery of an accelerator or platform build by a SCIKIQ pod, with phase gates.
Managed Services
We run and improve the platform, models and agents — DataOps, MLOps and AgentOps under SLAs.
A 30–45 day accelerator pilot proves value on one use case before you commit to scale.
Fixed scope, fixed timeline, agreed success criteria — and a scale-up business case at the end.
One accelerator & its agent squad
Typically forecasts, bid drafts and DSM watch for one trading portfolio, energy accounting for one circle, or the asset assistant for one asset class, with agents starting at “suggest” or “act with approval”.
2–3 source systems
e.g. smart meters, billing and the network GIS — or the maintenance system and technical documents.
One business unit
e.g. one circle, one plant or one pipeline section, with a named owner and SMEs for rules and UAT.
SCIKIQ pod
Engagement lead, data engineer, domain SME, AI / agent engineer.
Success-criteria targets are agreed in week 1.
Three steps take us from this conversation to value in production.
Start small with one accelerator, prove it, then scale on the same framework.
Scoping workshop
Walk through your data landscape and pain points, pick the pilot use case and agree success criteria.
1–2 weeksAccelerator pilot
Deploy one accelerator and its agent squad on the framework against live data, and measure the result.
30–45 daysScale & embed
Roll out across business units and further accelerators through project delivery or managed services.
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