Dedicated Energy AI
Intelligence that speaks
your energy language.
AI4Green builds dedicated energy-domain LLM solutions using your operational knowledge, authorised data, retrieval and domain adaptation — not a general chatbot bolted onto your business.
Guided demo
Four questions an energy assistant should handle well.
Choose a scenario to see the question, a prepared answer, the review steps that follow and the source context a deployed assistant would need.
Select a scenario to begin.
Each one shows a realistic question and the kind of grounded, reviewable answer we aim for.
How it is built
Three layers, in your context.
Your knowledge
Manuals, procedures, site notes and reporting definitions, retrieved with citations so answers can be checked.
Your energy context
Authorised operational data, asset registers and tariff structures, so answers reflect your sites rather than general theory.
Your AI experience
Interfaces, permissions and evaluation built for your teams, with domain adaptation on top of an existing foundation model.
Questions
How the AI work is grounded.
Do you train a foundation model from scratch?
No. We use domain adaptation: retrieval over your authorised knowledge, structured context, prompt and workflow design, evaluation, and where warranted fine-tuning of an existing model. Training a foundation model from scratch is not what this work involves.
Can it access our private documents?
Only the documents you deliberately make available, under access rules you define. Retrieval is scoped per user and per role, and the assistant cites what it used so answers remain auditable.
Does it recommend, or does it control equipment?
By default it explains and recommends. Any pathway to equipment control is a separate, deliberate design decision with explicit approval boundaries, logging and operator sign-off — never an implicit capability of the assistant.
How are answers evaluated and attributed?
We build an evaluation set from real questions your teams ask, score answers against it before and after changes, and require source attribution so a reviewer can trace any statement back to the data or document behind it.