AI For Power Markets: Trusted Data And Market Expertise

AI can help teams work through complex power market questions faster. But access to data alone does not tell an AI agent what that data means, how markets operate, or whether an answer is supported by the evidence.

Yes Energy brings trusted power market data together with decades of domain expertise, giving AI the context it needs to work more effectively with market information. Rigorous evaluation and guardrails help agents use the right data and tools, explain what they found, and acknowledge when they do not have enough information to answer.

How Yes Energy AI works

Built on More Than Data Access

Data

A data foundation maintained by market experts

Yes Energy collects, validates, and normalizes power market data as sources and markets change. Your AI workflows then run on a reliable foundation, so your teams don't have to stitch together separate feeds or reconcile the data themselves.

Context

Context that helps AI understand the market

Our semantic context layer connects the meaning behind the data: how datasets relate, which market conventions apply, and how a question should be interpreted. It helps AI work with power market information rather than simply retrieve rows of data.

Evaluation

Evaluations and guardrails for more reliable answers

We evaluate how agents interpret questions, select and use data and tools, and arrive at answers. Guardrails help prevent unsupported responses. When an agent lacks enough information or confidence, it will say so instead of inventing an answer.

Choose How You Work with AI

Use Yes Energy AI agents.

Ask questions and explore power market information through agents built on Yes Energy’s data and expertise. They offer a way to find relevant information, understand unfamiliar data and market concepts, and move from a question toward an informed next step.

Connect your own AI tools.

Bring Yes Energy’s power market data and context into the AI tools your team already uses through a secure MCP connection. Your team can work in its preferred environment while drawing on the Yes Energy foundation.

Frequently Asked Questions About Yes Energy AI

How is this different from connecting a general-purpose AI tool directly to power market data?

Data access tells an AI tool where to find information. It does not explain what a market term means, how datasets relate, which filters apply, or how conventions such as time zones and market intervals affect an answer. Yes Energy adds a semantic context layer that helps AI interpret those details and use power market data appropriately. We also evaluate agent behavior and apply guardrails to reduce unsupported answers.

How does Yes Energy evaluate the accuracy of AI answers?

We evaluate agents against real power market questions with answers reviewed by experts. We check the result and the steps used to reach it, including how the agent interpreted the question, selected data and tools, and applied query logic.

What happens if an agent cannot answer a question reliably?

Guardrails are designed to help the agent recognize when it lacks the information or confidence needed to support an answer. In those cases, it will say so rather than fill the gap with an invented response.

Does Yes Energy train AI models on customer data?

No. Yes Energy does not train AI models on customer data.

Can another customer see our questions or answers?

No. Your questions and the responses you receive are not shared with or surfaced to other customers.

Can we use our own AI tools instead of Yes Energy AI agents?

Yes. You can use Yes Energy AI agents or connect your own AI tools to the Yes Energy foundation through a secure Model Context Protocol (MCP) connection. Both paths draw on Yes Energy’s power market data and context. The right approach depends on where your team wants to work and how it plans to use AI.

What does Yes Energy provide that our team would otherwise need to maintain?

Yes Energy maintains the power market data and semantic context that help AI work with it. That includes managing changing data sources and market conventions, defining relationships and terms, and evaluating how agents use the data. Your team can focus its effort on its own workflows and expertise.