Services

Nine services, mapped to the energy value chain.

Energy companies don't buy "data and AI" — they buy lower AT&C losses, better collection, better prices on the exchange, fewer deviation charges, longer asset life, safer pipelines and filings that reconcile to the meter. Below, each SCIKIQ service line is mapped to the domains where it does that work — for discoms, generators and grid operators, oil and gas, and mining and metals, and the trading desk — with the use cases, the KPIs it moves and the accelerators behind it.

See the value-chain map How we add value, by domain Find your service by role
Chapter 2 · Services on the value chain

Where each service line works on the energy value chain

Read across a row to see where a service line leads and where it supports. Read down a column to see the team a utility or energy company gets in that domain. Select any domain to see use cases, the KPIs we help move and the working accelerators.

9
Service lines
8
Value-chain domains
16
Lead roles across the map
35
Supporting roles across the map
Leads the work Supports Hover a dot for detail · select a domain to explore it
01 · Advise
Data & AI StrategyStrategy
Data Governance & Regulatory DataGovernance & marketplace
02 · Build
Data Engineering & Platform ModernisationData products
AI & Agentic EngineeringAI & agents
03 · Transform
Power Trading & Market OperationsTrading
Distribution, Metering & Revenue ProtectionDiscom & revenue
Generation, Renewables & GridGeneration & grid
Oil, Gas & Asset IntegrityOil, gas & integrity
04 · Run
Managed Services: DataOps, MLOps & AgentOpsManaged
Service lines engaged 7 7 6 8 6 5 7 5

The mapping shows where each service line typically leads or supports; every engagement is scoped to the business. The value chain itself is explained on the overview.

How we add value

Domain by domain: from data to a measurable outcome

Each domain follows the same path — source data, a governed data product, AI and agents, an outcome the business measures. KPIs are the measures we help you move and track; we agree targets with you, we don't promise them in advance.

Domain 1 of 8

Generation & renewables

Solar and wind swing the daily schedule; every block is forecast and scheduled to the SLDC or RLDC, forecast errors turn into deviation charges and thermal units ramp harder than designed.

Data
SCADA, weather, schedules & settlements
Data product
RE forecast & plant-performance data products
AI & agents
Forecasting, scheduling & performance agents
Outcome
Lower deviation, better plant use

Energy use cases

  • Solar and wind forecasting by plant, pooling station and 15-minute block
  • Schedule and re-schedule options to the SLDC / RLDC that cut deviation exposure
  • Plant performance: availability, PLF, heat rate and auxiliary consumption
  • Curtailment and inverter / turbine underperformance detection

KPIs we help you move

Forecast accuracyDeviation chargesPlant load factorStation heat rate

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Data marketplace Generation agents
Domain 2 of 8

Transmission & grid operations

More renewables and inter-state flows make the grid harder to balance, while outages, loading and asset condition sit in separate systems.

Data
SCADA / EMS, trips, relays & inspections
Data product
Grid events data product
AI & agents
Congestion & root-cause agents
Outcome
Fewer trips, faster restoration

Energy use cases

  • Congestion and line-loading early warning
  • Trip and outage root-cause analysis
  • Transformer and substation health scoring
  • Inspection and relay-record extraction with GenAI

KPIs we help you move

Transmission availabilityTrips per monthRestoration timeTransformer failure rate

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Power EPC Enterprise Cockpit Grid agents
Domain 3 of 8

Power distribution, billing & consumers

AT&C losses decide discom viability; 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.

Data
AMI / MDM, feeder & DT, billing, payments, vigilance
Data product
Feeder-to-consumer energy accounting & meter-risk products
AI & agents
Theft, meter-health, collection & dispute agents
Outcome
Lower AT&C losses, faster cash

Energy use cases

  • Feeder- and DT-level energy accounting
  • Theft and meter-tamper detection with inspection lists for vigilance
  • Smart prepaid metering: data validation, balance alerts and estimated-bill reduction
  • Collection efficiency, dispute triage and payment / subsidy reconciliation

KPIs we help you move

AT&C lossesCollection efficiencyEstimated-bill shareTheft cases confirmed per inspection

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Revenue Assurance CLARION Discom agents
Domain 4 of 8

Power trading & markets

Power trades in fifteen-minute blocks across DAM, RTM, TAM, green segments, RECs, bilateral contracts and derivatives, while market coupling and deviation-settlement reform change the rules.

Data
Exchange results, schedules, positions & DSM
Data product
Price, load & position data products
AI & agents
Bid, DSM-watch & settlement agents
Outcome
Better prices, lower deviation

Energy use cases

  • Price and load forecasting for DAM and RTM
  • Bid preparation, optimisation and portfolio balancing
  • DSM exposure minimisation and imbalance watch
  • Green DAM / REC / VPPA strategy, hedging and settlement reconciliation

KPIs we help you move

Average power-purchase costDeviation chargesBid acceptance vs forecastSettlement breaks open

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Power markets in India Data marketplace CLARION Trading agents
Domain 5 of 8

Oil, gas & pipelines

From fields to refineries, pipelines, city gas and fuel retail, integrity and safety data is captured on paper and asset knowledge is locked in documents.

Data
Maintenance system, surveys, drawings & HSE
Data product
Asset & pipeline integrity data product
AI & agents
Asset assistant, survey & drawing agents
Outcome
Longer asset life, safer operations

Energy use cases

  • Voice-first, multilingual asset intelligence over maintenance records
  • Offline cathodic-protection surveys with anomalies flagged at capture
  • AI review of piping isometric drawings
  • Retail-outlet and city-gas demand analytics

KPIs we help you move

Equipment lifespanMaintenance decision timeSurvey anomalies closedHSE incidents

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Asset Intelligence Assistant Field Integrity Collector Isometric Drawing AI
Domain 6 of 8

Mining, metals & heavy industry

Steel, cement and mining run on furnaces, kilns and fleets where energy is the biggest cost and emissions the biggest scrutiny.

Data
Plant, kiln, quality & energy data
Data product
Plant performance data product
AI & agents
OEE, energy & emissions agents
Outcome
Lower energy and emissions per tonne

Energy use cases

  • OEE, downtime and kiln-utilisation analytics
  • Energy intensity and emissions per tonne
  • Quality defect root-cause analysis
  • AI questions over plant data

KPIs we help you move

OEEEnergy per tonneCO2 per tonneKiln utilisation

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Heavy-Industry Plant Cockpit ESG Intelligence Engine
Domain 7 of 8

Asset integrity & maintenance

Transformers, turbines, compressors and pipelines are inspected on calendars rather than condition, and maintenance history is free text.

Data
CMMS, sensors, inspections & failures
Data product
Asset health data product
AI & agents
Predictive & work-order agents
Outcome
Less downtime, longer asset life

Energy use cases

  • Condition-based and predictive maintenance
  • Voice-first answers over maintenance records and documents
  • Risk-based inspection planning
  • Spares optimisation by criticality

KPIs we help you move

Unplanned downtimeMean time between failuresPlanned maintenance shareSpares inventory value

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Asset Intelligence Assistant Asset agents
Domain 8 of 8

Finance, ESG & regulatory

Tariff filings must reconcile to the meter, energy cost decides margins at every site, and ESG and BRSR disclosures add numbers to defend.

Data
Filings, GL, energy cost & emissions
Data product
Governed finance & regulatory data products
AI & agents
Filing, cost & commentary agents
Outcome
Defensible filings, lower cost of energy

Energy use cases

  • Regulatory-filing data packs with lineage
  • Site and fleet energy-cost analytics and what-ifs
  • Plan-versus-actual variance with AI commentary
  • ESG, BRSR Core and emissions reporting

KPIs we help you move

Filing preparation timeCost per kWh by siteDays to closeDisclosure readiness gaps

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Energy 360 for Distributed Sites COMPASS ESG Intelligence Engine
Find your service

Start from your role

Each service line has a clear owner on the client side. Pick yours to jump to the services most teams like yours start with.

CMD / CEO
Chairman & Managing Director, CEO
Director (Ops)
Director (Operations / Technical), plant heads
Director (Comm.)
Director (Commercial), vigilance & revenue heads
Trading head
Head of trading, power purchase & scheduling
CFO
Director (Finance), regulatory & ESG heads
CIO / CDO
Chief Information & Chief Data Officer, head of OT
ESG & HSE
Sustainability, ESG and HSE heads
01

Advise

Set direction, make the business case and put the rules in place that data and AI must meet.

Advise · Service line

Data & AI Strategy

ForCMD / CEODirector (Operations)CIO / CDO
On the value chainGeneration & renewablesTransmission & gridDiscoms & consumersTrading & marketsOil, gas & pipelinesMining & metalsAsset integrityFinance & regulatory

The client problem

Smart-meter, SCADA and plant data is arriving faster than anyone can use it, and AI pilots multiply across circles, plants and fields without reaching production. There is no shared view of which use cases pay back, or who owns them.

Outcomes

  • A maturity baseline across 12 capability layers and five stages, scored on evidence rather than opinion
  • A prioritised roadmap from loss reduction to integrity and emissions, with a business case for each
  • An AI operating model that works across circles, plants, fields and contractors

What we do

  • Data & AI maturity assessment
  • AI strategy & use-case prioritisation
  • Loss-reduction and value-creation business cases
  • AI operating model & centre of excellence design

Typical engagements

  • AssessmentMaturity assessment and roadmap
  • Pilot · 30-45 daysProve the top-ranked use case on your data
  • BuildRoadmap delivered through our Build and Transform services
  • Managed runValue tracking and roadmap refresh

Delivered with

Maturity assessment Energy 360 for Distributed Sites Value calculator
Advise · Service line

Data Governance & Regulatory Data

ForCDORegulatory affairsCompliance & ESG
On the value chainGeneration & renewablesTransmission & gridDiscoms & consumersTrading & marketsOil, gas & pipelinesMining & metalsFinance & regulatory

The client problem

Tariff petitions, true-ups, reform-scheme milestones and ESG disclosures ask for numbers that reconcile to the meter and the plant. Consumer data now falls under the DPDP Act, and AI adds a new layer to govern.

Outcomes

  • A governed data marketplace where teams discover, request and subscribe to data products, with approvals
  • Critical data elements with owners, lineage and data-quality controls for regulatory filings
  • Consumer-data consent and protection aligned to the DPDP Act
  • AI and agent governance: policy, approvals, evaluation and a kill switch

What we do

  • Data marketplace: catalog, glossary, subscriptions & approvals
  • Data contracts, ownership & access policy for every data product
  • Regulatory-filing data lineage (CERC / SERC)
  • Consumer-data privacy (DPDP Act) & ESG / BRSR Core lineage
  • AI & agent governance

Typical engagements

  • AssessmentGovernance and lineage gap review against filings
  • Pilot · 30-45 daysCatalogue, lineage and DQ for one filing or disclosure
  • BuildCDO function, metadata repository and controls
  • Managed runOngoing DQ monitoring and stewardship

Delivered with

Data marketplace & product factory Data Governance Hub ESG Intelligence Engine Agent governance
02

Build

Engineer the data platforms and the AI that runs on them, with governance built in.

Build · Service line

Data Engineering & Platform Modernisation

ForCIOCDOHead of OT
On the value chainGeneration & renewablesTransmission & gridDiscoms & consumersTrading & marketsOil, gas & pipelinesMining & metalsAsset integrityFinance & regulatory

The client problem

SCADA, meter data management, billing, GIS, plant historians and ERP were bought separately and never agree. Billing and operational feeds disagree on which sites even exist, and every new report starts with another extract.

Outcomes

  • A data product factory that publishes forecasts, market, meter and asset data as governed, versioned products
  • One governed, cloud-agnostic data platform joining OT and IT data
  • A site, asset and consumer master that reconciles billing with operations
  • Batch, streaming and IoT pipelines with lineage from source

What we do

  • Data product factory: contracts, SLAs, quality checks & versioning
  • OT + IT + market integration: SCADA, AMI / MDM, exchange & DSM data
  • Billing, GIS and ERP data models
  • Streaming and IoT pipelines
  • Lakehouse / warehouse on Azure, AWS or on-premise
  • Infrastructure as Code & legacy modernisation

Typical engagements

  • AssessmentData estate and target-architecture review
  • Pilot · 30-45 daysConnect and reconcile priority feeds end to end
  • BuildPlatform build in sprints, tested with real data
  • Managed runDataOps under agreed SLAs

Delivered with

Data marketplace & product factory SCIKIQ Data Fabric Energy 360 for Distributed Sites
Build · Service line

AI & Agentic Engineering

ForCIODirector (Operations)Head of asset management
On the value chainGeneration & renewablesTransmission & gridDiscoms & consumersTrading & marketsOil, gas & pipelinesMining & metalsAsset integrity

The client problem

GenAI and agent pilots stall at the safety review. In a business where a wrong action can black out a feeder or breach a pipeline, there are no autonomy limits, no audit trail and no named owner for the exceptions.

Outcomes

  • Supervised agents that observe, flag and draft — never switch, control or dispatch on their own
  • GenAI that reads maintenance records, technical documents, drawings and inspection reports, in more than one language
  • People review only the exceptions, with the agent's draft and evidence in front of them

What we do

  • Agent design: roles, squads & autonomy levels
  • Multilingual and voice GenAI over technical documents
  • AI vision for drawings and inspection photos
  • Forecasting and anomaly models
  • Evaluation & guardrails: policy, limits, kill switch

Typical engagements

  • AssessmentAgent opportunity and safety-controls review
  • Pilot · 30-45 daysOne agent squad working real cases under your controls
  • BuildSquads integrated with your systems and scaled
  • Managed runAgentOps: monitoring, overrides, drift

Delivered with

Asset Intelligence Assistant Isometric Drawing AI Run an agent live Governance & controls
03

Transform

Domain practices that change how an energy business works, end to end, with our accelerators as the starting point.

Transform · Service line

Power Trading & Market Operations

ForHead of trading / power purchaseScheduling deskCFO & risk
On the value chainGeneration & renewablesTrading & marketsFinance & regulatory

The client problem

Power is bought and sold in fifteen-minute blocks across DAM, RTM, TAM, green segments, RECs, bilateral contracts and now derivatives, while market coupling and deviation-settlement reform change the rules. Desks bid off spreadsheets and forecasts they don’t fully trust, find DSM exposure after the fact and reconcile settlements by hand.

Outcomes

  • Load, price and RE forecasts published as data products the desk trusts by gate closure
  • DAM / RTM bids and portfolio positions drafted with the evidence; a trader submits every one
  • DSM exposure watched through the day, and settlements reconciled daily with every break explained

What we do

  • Price & load forecasting for DAM and RTM
  • Bid preparation, optimisation & portfolio balancing
  • DSM exposure minimisation & imbalance watch
  • Green DAM, REC & VPPA strategy; derivatives hedging analytics
  • Market-coupling readiness
  • Settlement & reconciliation

Typical engagements

  • AssessmentTrading-desk data and decision diagnostic
  • Pilot · 30-45 daysForecasts, bid drafts and DSM watch for one portfolio
  • BuildTrading data products, desk cockpit and agents
  • Managed runForecast and settlement squads under SLA

Delivered with

Data marketplace & product factory CLARION Trading agent squad Power markets in India
Transform · Service line

Distribution, Metering & Revenue Protection

ForDiscom MDDirector (Commercial)Chief vigilance officer
On the value chainDiscoms & consumersTrading & marketsAsset integrityFinance & regulatory

The client problem

AT&C losses, estimated bills, theft and slow collections decide whether a discom is viable. Smart prepaid metering under RDSS creates the data to fix it, but feeder, meter, billing and payment systems don't yet speak to each other.

Outcomes

  • Energy accounting from feeder and distribution transformer to consumer
  • Theft and tamper cases prioritised for vigilance teams with the evidence attached
  • Billing, collection and subsidy reconciled daily, with disputes triaged

What we do

  • Feeder- and DT-level energy accounting
  • Theft, tamper and meter-health analytics
  • Smart prepaid metering (RDSS): data validation, balance alerts & estimated-bill reduction
  • Collection efficiency, arrears & payment / subsidy reconciliation
  • Consumer complaint & dispute triage

Typical engagements

  • AssessmentAT&C loss and revenue-protection diagnostic
  • Pilot · 30-45 daysEnergy accounting and theft scoring for one circle or division
  • BuildRevenue-protection platform rolled out across circles
  • Managed runLoss analytics and vigilance squads under SLA

Delivered with

CLARION Revenue Assurance Discom agent squad
Transform · Service line

Generation, Renewables & Grid

ForDirector (Generation)Head of schedulingGrid operations
On the value chainGeneration & renewablesTransmission & gridTrading & marketsAsset integrity

The client problem

Solar and wind make every fifteen-minute block harder to forecast and schedule; forecast errors become deviation charges, and grid events, loading and asset condition sit in separate systems.

Outcomes

  • Plant-level generation forecasts the scheduling desk can trace
  • Scheduling options that reduce deviation exposure
  • Grid events, loading and asset health on one timeline

What we do

  • Solar and wind forecasting by plant, pooling station and block
  • Scheduling to SLDC / RLDC and deviation analytics
  • Plant performance: availability, PLF and curtailment
  • Thermal heat-rate and auxiliary-consumption analytics
  • Congestion and outage root-cause analysis
  • Green open-access and REC tracking

Typical engagements

  • AssessmentForecasting, scheduling and grid-data review
  • Pilot · 30-45 daysForecasts for a set of plants against actual schedules
  • BuildForecasting and grid analytics platform
  • Managed runModel operations and retraining under SLA

Delivered with

Control Tower Energy 360 for Distributed Sites Generation agent squad
Transform · Service line

Oil, Gas & Asset Integrity

ForDirector (Operations)Head of integrityPlant heads
On the value chainTransmission & gridOil, gas & pipelinesMining & metalsAsset integrity

The client problem

Pipelines, plants, gas networks and heavy-industry assets carry safety-critical risk, yet surveys are captured on paper, designs checked by hand, and asset knowledge locked in maintenance systems and documents.

Outcomes

  • Field integrity data clean at source, with anomalies flagged at capture
  • Maintenance and engineering knowledge answerable by voice, in more than one language
  • Plants, kilns and emissions on one view, from the furnace to the board

What we do

  • Asset intelligence over maintenance systems & documents
  • Offline pipeline-integrity survey capture
  • AI review of piping isometric drawings
  • Predictive and risk-based maintenance
  • Heavy-industry plant and emissions analytics

Typical engagements

  • AssessmentIntegrity, maintenance and plant-data diagnostic
  • Pilot · 30-45 daysAsset assistant or field collector on one asset group
  • BuildIntegrity and plant platforms across sites
  • Managed runModel and app operations under SLA

Delivered with

Asset Intelligence Assistant Field Integrity Collector Isometric Drawing AI Heavy-Industry Plant Cockpit
04

Run

Keep platforms, models and agents healthy and improving after go-live.

Run · Service line

Managed Services: DataOps, MLOps & AgentOps

ForCIODirector (Operations)
On the value chainGeneration & renewablesTransmission & gridDiscoms & consumersTrading & marketsOil, gas & pipelinesAsset integrity

The client problem

After go-live, meter and SCADA feeds change, forecasts drift with the weather and the season, and agents need someone watching overrides and limits around the clock — because the grid never closes.

Outcomes

  • Platforms, pipelines, models and agents run under agreed SLAs
  • Continuous improvement driven by override and evaluation data
  • Your teams freed from L2/L3 support

What we do

  • Run & L2/L3 support
  • DataOps, including meter, SCADA and billing feeds
  • MLOps for forecasting and anomaly models
  • AgentOps: logs, overrides, drift, kill switch
  • Continuous improvement

Typical engagements

  • AssessmentRun-readiness and support model review
  • Pilot · 30-45 daysHypercare for a newly live capability
  • BuildMonitoring, runbooks and SLAs
  • Managed runOngoing service under agreed SLAs

Delivered with

Governance & controls SCIKIQ Data Fabric
Our assets

What makes our services faster

Every engagement starts from SCIKIQ IP rather than a blank page. These assets are how we deliver — they come with the service.

1
SCIKIQ Data Fabric

The governed foundation every engagement runs on: the 4C method (Connect, Curate, Contextualize, Consume), 268 data sources, and governance, lineage, data quality, PII detection and masking, an AI/agent layer and security built in — cloud-agnostic on Azure, AWS or hybrid.

Explore the framework
2
Accelerators

Energy accelerators from platforms we have built — the Asset Intelligence Assistant, Energy 360 for Distributed Sites, the Field Integrity Collector and the Heavy-Industry Plant Cockpit — plus cross-industry accelerators such as CLARION, COMPASS and NARRATOR, tailored to your rules, data and controls.

Accelerators by service line
3
Supervised digital workforce

AI agents that plan, call tools and gather evidence on the governed data. Policy decides what goes straight through; people approve everything else; every step is logged and a kill switch halts all agents.

How it works Watch one work
How we engage

From a business outcome to measured value

Every engagement starts from the business outcome, not the technology. We frame it through the same business lens each time, then deliver in five phase-gated stages. Most utilities and energy companies start with a discovery and value case for one value-chain domain, then scale to the next on the same foundation.

How we frame an engagement

  1. 1Business outcome & KPI
  2. 2Value-chain domain
  3. 3Decisions
  4. 4Data
  5. 5AI & agents
  6. 6Governance & adoption
  7. 7Measured value

Delivery phases

Phase 1
Discover & value case

Outcome, domain and KPIs agreed; data and process assessment; baseline and business case.

Phase 2
Design

Decisions, data products, models, agents and controls designed for the chosen domain.

Phase 3
Build & integrate

Sprint delivery on the SCIKIQ Data Fabric, integrated with SCADA, meter data, billing, GIS, plant historians, maintenance systems and ERP; tested on real data.

Phase 4
Deploy & adopt

Go-live, people and process change, agent autonomy limits agreed with operations, quality, procurement and finance; value tracked against the baseline.

Phase 5
Run & scale

Managed service — DataOps, MLOps and AgentOps under SLA — and the next domain on the same foundation.

Each phase ends with a gate signed off by your steering group; a pilot in one domain typically reaches a production-ready capability in 30-45 days. The SCIKIQ Data Fabric we build on

Engagement models

Staff Augmentation

Data engineers, architects, analysts and AI specialists embedded in your teams, under your delivery lead.

Project Delivery

Outcome-based delivery of an accelerator or platform build, owned end-to-end by SCIKIQ with phase gates.

Managed Services

We run and improve your data platforms, models and agents — DataOps, MLOps, AgentOps and support under agreed SLAs.

Weekly status Bi-weekly steering Phase-gated sign-off 30-45 day pilot → scale

Start with the outcome you need

Pick a domain and a KPI. We'll propose a discovery and value case or a 30-45 day pilot and show you what the first weeks look like.

Talk to us Take the self-assessment
Next chapter · 3 of 6
The foundation

Every domain above runs on the same governed data fabric — SCADA, meters, billing, plants, pipelines and the ledger connected, curated, contextualised and consumed, with lineage from source to regulatory report.

Next chapter: The foundation