AI for energy and critical infrastructure

Assistant for operating and emergency procedures

Knowledge and documents

Problem: Operating and emergency procedures are scattered, and in a critical situation what counts is how fast you reach the right one

How it works: The central RAG knowledge base points to the right procedure, citing the relevant passage of the source document

What you gain: The dispatcher gets the right procedure with a citation in seconds — in a situation where searching through folders is not an option

Controlled rollback of AI agent actions

Process automation

Problem: An agent has carried out a sequence of operations that must be undone immediately

How it works: A DAG-based rollback of agent actions — the user sees the rollback plan and confirms the operation

What you gain: A wrong sequence of agent actions is reversed in a controlled way, with a visible plan — no manual unwinding after the fact

Searching grid diagrams and technical documentation

Knowledge and documents

Problem: Grid diagrams and technical documentation are not text-searchable

How it works: Advanced OCR that describes drawings and charts in a form suited to the model

What you gain: A grid diagram can be found with a question, not by browsing an archive of drawings

AI adoption in a dispersed organisation

Adoption and skills

Problem: Staff spread across branches do not adopt new tools

How it works: AI Champions in every branch and the CDF Academy with internal certification (CDF-F3)

What you gain: Every branch has its own change leader and trained users — the tool takes hold in the field, not only at headquarters

Sovereign AI for critical infrastructure

Security and sovereignty

Problem: Data from critical infrastructure must not leave the company

How it works: Sovereignty model (CDF-F1) — Level L4, isolation from the public network; routing only to models in the company’s server room; Energy sector profile

What you gain: AI runs entirely in the company’s server room — and you can document this for auditors and the regulator

A secure AI environment instead of public tools

Security and sovereignty

Problem: Staff use public AI tools outside the organisation’s control

How it works: A central gateway as the single point for model calls, with a usage register; a scan for unauthorised AI tools and a path to legalise them

What you gain: The company knows who uses AI and for what; staff get a legitimate alternative instead of a ban

Evidence of AI oversight for NIS2 and the KSC Act

Compliance and oversight

Problem: An NIS2 and KSC audit requires evidence of oversight over AI

How it works: A register of AI agents with an owner, autonomy level and emergency shutdown procedure; an immutable event log with export

What you gain: Evidence of AI oversight is produced by the system as you work, not assembled before the audit

What legal framework ensures a safe deployment?

The AI Act, the amended KSC Act (NIS2) and an internal AI policy linked to the information security management system. The CDF methodology guides you through the Initial Assessment, the classification of use cases and documents you can show an auditor

On-premises or cloud AI?

For critical infrastructure data — on-premises. At LW Bogdanka, TWIN:DESK runs entirely in the client’s infrastructure, with no data going outside, and the deployment took two months

How do you organize historical data and access to it?

Start with the documents people search for most often, e.g. operating and emergency procedures, and assign permissions by department. At LW Bogdanka, about 80% of the knowledge base consisted of scans, which TWIN:DESK processed for search with citations