Standalone LLMs reach only 5–17% accuracy on enterprise schemas.
PRISM delivers a stable 98–100% through a data meaning dictionary and agent orchestration.
(measured across 500+ internal test iterations)
Even the same LLM produces different results depending on the system it runs on. PRISM completes enterprise AI analysis with a data meaning dictionary and AI agent analysis.
On enterprise schemas — hundreds of tables and complex joins — standalone LLMs reach only 5–17% accuracy.
The model lacks business context, cannot disambiguate column names, and has no mechanism to validate its own output.
PRISM's data meaning dictionary translates physical schemas into business language.
Columns map to business terms, tables group into domain models, and accuracy on the query layer reaches 98–100%.
Agent orchestration handles interpretation, follow-up questions, and judgment.
Even when individual LLM calls falter, the system delivers a stable 98–100% accuracy across all major models.
Good architecture produces better answers.
PRISM does not replace AI.
It surrounds AI with the structure enterprises require — so the model can focus on what it does best.