Sovereign AI for renewable fleets

A foundation model that learns how your fleet actually behaves.

Breschna.AI builds domain-specific AI for wind, solar and battery operators. It is trained on your whole fleet, deployed inside your own data platform, and hosted in Australia.

Talk to us See how a pilot works
Same wind, 30-day average. T-15 · vane 10° off · −3% output · no alarm. One turbine quietly losing about 275 MWh a year. Illustrative.

The problem

Fleets lose energy to faults no single-site tool can see.

Operators are adding sites, makers and asset types faster than their analytics can keep up. The signal is in the data. It is spread across too many formats and too few failures per site.

Every maker speaks its own language

SCADA from different turbine makers arrives in different formats, so turbines are rarely compared on one scale.

One site's history is too thin

A site sees a few major failures a decade. New turbines start with none. There is too little to learn from in isolation.

Today's AI sees a slice

OEM portals see their own turbines. General AI assistants have never seen a turbine. Nothing learns from the whole fleet at once.

~50 GWh

A year of extra clean energy from recovering just 1% of output across a 1.8 GW wind fleet, with no new turbines.Illustrative: 33% capacity factor.

The fleet model

Pretrain once on the whole fleet. Tune small heads for each question.

The same recipe that made language models general, applied to turbine signals and maintenance records.

In

All fleet data, together

  • 10-minute SCADA
  • Alarms and events
  • Work orders and notes
  • Inspections and weather

Core

Time-series core + language layer

Learns what normal looks like for each turbine type by hiding and predicting chunks of fleet data, then judges every turbine against its neighbours in the same wind.

Fleet health scoreMaintenance budget goes to the worst turbines, any make
Cross-site fault warningsA failure at one site is watched for everywhere
Day-one monitoringNew sites start with the fleet's knowledge
Repair verificationOutput before and after every work order

Why your own model

Sovereignty, and sharper intelligence at a cost you control.

Sovereign and secure

Australia's grid data, Australia's model.

  • Trained and hosted in Australia, inside the operator's own data platform.
  • Turbine, grid and maintenance data never go to offshore AI services.
  • Reads a read-only historical copy. No path to live OT control systems.
  • You keep the model weights, whatever happens to us.

Better intelligence, controlled cost

Right-sized, domain-trained, always learning.

  • Learns turbine physics, alarms and repair history that general models have never seen.
  • A compact time-series core plus an open-weight language layer, adapted rather than trained from scratch.
  • No per-token fees to frontier models with hundreds of billions of parameters.
  • Every confirmed fault and repair outcome retrains the model on a fixed compute budget.

Where we fit

Agents are the interface. A domain model is the intelligence.

We work alongside the tools operators already use. Our model exposes an MCP endpoint, so your own agents and your vendors' agents can ask it questions.

AI agents and assistantsYour copilots and vendor agents ask the questions
APM and OEM toolsReporting, benchmarking and service portals you keep using
Breschna.AI fleet modelLearns how your fleet behaves and answers through MCP
Your data platformSCADA, alarms and work orders, where they already live

A pilot you can score

Replay your history. See what the model would have caught.

You do not have to take the model on faith. A pilot uses data you already hold and a test you control.

  1. You pick

    The problem worth testing, and one site's 10-minute history from the last few years.

  2. We run blind

    We pretrain and replay the period without seeing your incident log.

  3. Score it together

    What it caught, how early, and how many false alarms, against your current alarms.

The team

Foundation-model science meets energy markets and operations.

Abdul Karim, PhD

Model science

Trained foundation models as AI Research Lead at Leonardo.AI, acquired by Canva. Applied AI at Microsoft. 12+ years in machine learning.

Maryam Hasani Shoreh, PhD

Time series & predictive maintenance

Senior AI Consultant at Fujitsu. Built predictive maintenance, anomaly detection and forecasting models. PhD in computer science, University of Adelaide.

Khushal Khan

Energy markets & commercial

Managing Director, Breschna Renewables. Former commercial roles at AEMO Services and Energise Renewables. Master's in Sustainable Energy Engineering, RMIT.

Contact

Run a wind, solar or battery fleet? Let's talk.

We are working with our first Australian operators and have room for a small number of pilot partners.

Email

hello@breschna.ai