Definition · AI in finance
Inference
Inference is running a trained model to generate output. For inference, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Also known as AI inference, model inference
Why it matters
Understanding inference matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo runs model inference to interpret questions and draft analysis, but routes every numeric claim through a deterministic engine that produces auditable results.
In practice
Governance example
Teams use inference when they evaluate whether an AI-assisted analysis can be trusted. The useful test is whether the output is tied to approved data, repeatable logic, human review, and an audit trail.
Pluvo example
Pluvo runs model inference to interpret questions and draft analysis, but routes every numeric claim through a deterministic engine that produces auditable results.
In practice, teams should define inference with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding inference matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo runs model inference to interpret questions and draft analysis, but routes every numeric claim through a deterministic engine that produces auditable results.
A strong workflow for inference separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
Pluvo runs model inference to interpret questions and draft analysis, but routes every numeric claim through a deterministic engine that produces auditable results.
FAQ
What is inference in machine learning?
Inference is running a trained model to generate output. For inference, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
What is the difference between training and inference?
The boundary for inference differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers what inference means — running a trained model to generate output — and how it differs from training the model, so teams should compare those boundaries before using it in reporting or planning.