Definition · AI in finance
Semantic search
Semantic search is finding results by meaning rather than keyword match using embeddings. For semantic search, 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 vector search, similarity search
Why it matters
Understanding semantic search 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 resolves questions through a governed semantic model and ontology rather than semantic search over text, so the same question always returns the same computed answer.
In practice
Governance example
Teams use semantic search 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 resolves questions through a governed semantic model and ontology rather than semantic search over text, so the same question always returns the same computed answer.
In practice, teams should define semantic search with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding semantic search 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 resolves questions through a governed semantic model and ontology rather than semantic search over text, so the same question always returns the same computed answer.
A strong workflow for semantic search 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 resolves questions through a governed semantic model and ontology rather than semantic search over text, so the same question always returns the same computed answer.
FAQ
What is semantic search?
Semantic search is finding results by meaning rather than keyword match using embeddings. For semantic search, 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.
How does semantic search differ from keyword search?
To use semantic search, start with the decision, then confirm the source data, timing, calculation logic, and owner. The analysis is strongest when a reviewer can trace the answer back to the records that produced it.