What makes Clir's AI different?

It's tempting to assume any team could wire their own AI assistant to their data and get the same result. They can wire it up in a weekend. However, the answers won't hold. The difference between Clir and the alternatives isn't the AI; it's everything that has to be true before the AI answers:

The data is codified, contract by contract.

SCADA data, contractual terms, OEM communications and service reports are structured and aligned before any model runs. A hundred sites means a hundred different definitions of availability and loss and aligning each one to its specific contract is slow, expert work that doesn't transfer between portfolios and can't be prompted into existence. It's the step a generic AI skips and an internal build underestimates.

The predictive layer sits on a baseline that’s hard to acquire.

Clir's forecasts and benchmarks run against 350+ GW of operational data across wind, solar and battery storage. A model trained only on your own portfolio can compare you to yourself; it can't tell you whether a gearbox failure rate is normal across the fleet, because it has never seen the fleet.

The intelligence is yours alone, and it compounds.

The model trains on your specific contracts, OEMs and operational history, getting more accurate every reporting cycle and is never shared with another Clir client. The gap with anyone starting later only widens.

The connection is open; the foundation is not.

Clir uses the open Model Context Protocol so it works with any assistant but the protocol is the commodity. Anyone can stand up a server. What makes Clir's valuable is the two layers beneath it. Anyone can copy the connector. No one can copy what it's connected to.

How does Clir's three-layer AI architecture work?

Clir's AI Layer is structured across three sequential layers. Each layer depends on the one beneath it. All three are needed to create a seamless experience of data intelligence baked right into the owners’ tech stack that their team can easily use.

Layer 1: Data Foundation

SCADA systems report what is happening at a wind, solar or BESS asset. They do not report why. A turbine going offline appears in SCADA as a status code. The reason it went offline lives in field reports, inspection notes and OEM communications outside the SCADA system entirely.

Clir's data layer closes that gap. AI-driven extraction pipelines ingest daily reports, weekly summaries, board packs, OEM communications and operational notes, structuring them and aligning them with live SCADA data so that every event carries the contextual story of what happened on site.

Clir manages extraction reliability through an ensemble approach across multiple AI models, with results flagged for review by Clir's data engineering and renewables expert teams when confidence is low.

One hundred sites means one hundred different contractual definitions of availability and loss factors. The codification work that aligns those definitions to each specific contract is what makes every downstream output accurate and defensible.

Layer 2: Predictive Intelligence

Once data is codified, machine learning and physical models run against it. Clir's predictive layer produces energy yield forecasts, component failure predictions, performance benchmarks and long-term budget reforecasts. Every result runs against an industry baseline drawn from 350+ GW of operational data across wind, solar and battery storage, giving each client's findings the comparative context their own data alone cannot produce.

Predictive accuracy improves with each reporting cycle. The model trains on the client's specific operational patterns, contracts and OEM history, making the intelligence layer more accurate over time.

Layer 3: AI Assistant Integration

Most renewable energy teams already have a corporate AI deployed: Copilot, Claude, ChatGPT or another assistant. Clir's MCP server connects each client's codified data and predictive models to whichever of those tools their team uses.

When a team member asks their AI assistant a question about their portfolio, the assistant calls the Clir MCP server, retrieves the relevant analysis and delivers the result directly into the conversation. No custom integration needed for each tool.

Every AI response includes deep links back into the Clir platform so analysts can verify findings and examine underlying data at any point. Clir remains the system of record, and the AI assistant is a fast-tracked, conversational interface into it.