Generative AI: Energy and Utilities

Generative AI for energy and utilities

Utilities run on documents as much as on wires and pipes: switching procedures, equipment manuals, inspection reports, tariffs, interconnection applications and regulatory filings. We build generative AI that reads them, answers from them and drafts from them, for field crews, control room support staff and customer teams, while keeping AI well away from anything that operates the network. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

Discuss a utility AI use case Energy and utilities software

Key highlights

We build field copilots, outage and customer service agents, billing assistants and regulatory document AI for utilities and energy companies.
AI reads from your asset, work order and document systems and never writes to SCADA, ADMS or any control system.
Answers are grounded in your own procedures and manuals, cite the page they used, and refuse when the source is missing.
Models run through enterprise APIs in your chosen region or self hosted in your own cloud for critical infrastructure data.
A first use case, such as a maintenance manual copilot for one asset class, usually reaches a guarded pilot in 8 to 12 weeks.

Why AI projects stall in energy and utilities

The use cases are obvious. What stops them is security review, critical infrastructure rules, and engineering staff who will not trust an answer they cannot trace.

Security teams say no to anything near OT. Any proposal that sounds like AI touching the grid stalls in review. Projects need an architecture that clearly keeps models out of control networks and only reads business data.

Engineering knowledge leaving with retirees. Decades of know how sit in manuals, old inspection notes and the heads of senior engineers. New technicians spend hours searching shared drives for the right procedure.

Contact centres flooded during outages. Storms and planned works drive call spikes. Customers want restoration times and status, and agents give inconsistent answers because the information changes by the minute.

Complex tariffs and billing disputes. Time of use tariffs, estimated reads, feed in credits and back billing rules make bill queries slow to resolve and easy to get wrong, which turns into regulator complaints.

Interconnection and permitting backlogs. Solar, storage and EV charger connection requests arrive as long, inconsistent applications. Engineers spend their time checking documents for completeness instead of studying the network.

Regulatory reporting by copy and paste. Rate cases, performance reports and incident submissions are assembled by hand from many systems, under deadline, with little reuse from one filing cycle to the next.

Use cases

Generative AI use cases we build for energy and utilities

Each use case reads from business systems, keeps a human responsible for action and is measured against a test set before it reaches staff.

Field technician copilot

RAG over OEM manuals, switching and safety procedures, asset history and past work orders, on a tablet or phone that works with weak signal. Technicians ask in plain language and get the cited procedure step, not a guess.

Outage communication agent

A tool using agent reads the outage management system for affected areas and estimated restoration times, answers customers by chat, SMS or voice, and hands over to a person for medical priority or safety reports.

Billing and tariff assistant

A copilot for contact centre agents that reads the customer account, meter reads and tariff rules from the CIS, explains the bill line by line and drafts a compliant response for the agent to approve.

Interconnection and permit document AI

Document extraction checks DER and EV charging applications for completeness, pulls equipment ratings and site details into structured fields, and drafts requests for missing information for an engineer to review.

Inspection and work order summarisation

Turns free text inspection notes, drone and vegetation survey findings and technician comments into structured defects and draft work orders in Maximo or SAP PM, with a planner approving every one.

Regulatory filing and knowledge search

Search and drafting assistance across past rate cases, regulator decisions, internal policies and performance data, producing first drafts of report sections with every figure linked to its source.

How we deliver

From one use case to an enterprise AI capability

01
Use case discovery and data readiness
We rank use cases by value and risk with operations, customer and security teams, then check the data: document quality, asset and work order history, CIS access and which systems are in scope for critical infrastructure rules.
02
Evaluation harness and prototype
Engineers and agents label a golden set of real questions, documents and bills. The prototype is scored on accuracy, citation correctness and safe refusal on every change.
03
Guarded pilot with a human in the loop
One depot, one contact centre team or one application queue uses the tool for real work. Every answer can be flagged, and every flag improves the test set.
04
Production rollout with monitoring
Wider rollout with SSO, role based access, usage and cost dashboards, drift alerts and full logging of prompts, sources and outputs for audit and incident review.

AI governance for energy and utilities

Utilities are critical infrastructure, so AI governance starts with network separation and ends with evidence a regulator or auditor can follow.

No AI in control loops NERC CIP awareness (North America) NIS2 (EU) EU AI Act IEC 62443 aligned segmentation GDPR and UK GDPR Smart meter data privacy Data residency by region Self hosted model option

AI stays out of the control network. Models read from IT systems such as the CIS, OMS exports, asset registers and document stores, and never connect to SCADA, ADMS or protection systems. Where AI drafts a switching or maintenance step, a qualified person approves it in the existing process. Under the EU AI Act, AI used as a safety component in managing electricity, gas, water or heating supply is classed as high risk. The EU has been revising when those obligations apply, so we design for them now rather than waiting for the final date. We design so that our use cases sit outside that boundary or meet it deliberately.

Customer and meter data is protected. Smart meter readings can reveal how people live, so we redact PII before external model calls, keep data in your chosen region, and offer self hosted open weight models in your own cloud where policy requires.

Every output is traceable. Each answer stores the model, prompt, retrieved sources and the user who acted on it. Customer facing agents disclose that they are AI, as EU AI Act transparency rules require from August 2026, and escalate vulnerable customers to people.

Models and tooling we use for utility AI

Model choice is made per use case after testing on your documents. Integration adapters connect to the utility systems you already run.

Anthropic Claude OpenAI GPT models Azure OpenAI and AWS Bedrock Llama and Mistral self hosted LangGraph agents pgvector and Elasticsearch hybrid search Azure Document Intelligence and Textract IBM Maximo and SAP PM integration SAP IS-U and Oracle CC&B integration Twilio and SIP voice agents Langfuse tracing and evals Python and Node.js

FAQ

Common questions

Will the AI ever control grid or plant equipment?

No. Our generative AI systems read business data and assist people. They do not connect to SCADA, ADMS, DCS or protection systems, and any operational action stays with a qualified person in your existing process. This separation is documented for your security team before build starts.

Where does our data go, and can we keep it in country?

It stays in the region you choose. We use enterprise model endpoints with no training on your data, hosted in your preferred cloud region, or open weight models self hosted in your own account. PII is redacted before any external call where required.

How do you handle hallucinations in safety related answers?

By grounding every answer in your approved procedures and manuals, quoting the source passage, and refusing when no source supports an answer. Safety critical content is tested against a labelled question set on every release, and technicians always see the original document.

How long does a first utility AI project take?

Usually 8 to 12 weeks to a guarded pilot for one use case. That includes discovery, a labelled test set, the prototype and a pilot with one team. Enterprise rollout and further integrations follow in phases.

Should we buy an AI product or build our own?

Buy when a vendor feature inside your CIS, CRM or asset system covers the need. Build when the value is in your own procedures, asset history and tariffs, or when several systems must be combined. We often extend bought platforms with custom retrieval and agents.

How is a utility AI project priced?

Usually as a fixed price discovery and prototype phase, then a pilot and rollout scoped from measured results. Running costs depend on usage volume and model choice, and we estimate them per use case before you commit. See our pricing page for engagement models.

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