Introducing Osprey AI

Industrial PI environments contain many assets and calculations that are difficult to understand without local knowledge. Osprey AI was designed to help teams create a plain-language explanation of that existing context.

The capability can summarize AF assets and explain analysis logic based on the available configuration and metadata.

Explain an AF asset

An AF element can contain a name, template, attributes, analyses, and relationships. Osprey AI uses this information to produce a short description of the asset and its role in the model.

This can help a new engineer understand:

• What the element represents

• Which measurements are associated with it

• Which calculations are connected to it

• Which displays or dependencies reference it when that context is available

The explanation is a starting point. Engineers should verify important technical details against the AF configuration and plant documentation.

Explain an AF analysis

Complex analyses can be difficult to interpret, especially when they use several inputs or nested dependencies.

A useful AI explanation should identify:

• Purpose of the calculation

• Inputs

• Major functions or logic

• Output

• Schedule or trigger when available

• Upstream dependencies

The system should not claim to know the process intent when the configuration does not provide enough evidence.

Reduce dependence on tribal knowledge

Many PI systems contain logic that was created by engineers who no longer support the environment.

AI-assisted explanations can reduce the time required to understand inherited calculations. They can also help PI administrators communicate technical logic to process, reliability, and data teams.

Use AI as an assistant, not an authority

An AI-generated explanation can be wrong or incomplete. It should remain traceable to the configuration that produced it.

For engineering, safety, maintenance, or regulatory use, a qualified person must validate the result.

A practical use of AI in the PI System

The strongest use case is not autonomous engineering. It is faster interpretation of existing system context.

When AI can explain an asset or calculation and show the underlying source, teams can document systems faster, onboard engineers more efficiently, and spend less time reverse engineering old configurations.