AI that answers from specific, authoritative sources supplied at question time, and shows those sources, rather than relying only on what a model learned in training.
By retrieving the relevant records for each question, instructing the model to answer only from them, requiring citations, and testing accuracy against real cases before and after launch.
We are model-pragmatic. The architecture keeps your data and access controls in the integration layer, so the model can be changed as needs, costs, and approvals evolve.
Not in our designs. Retrieval runs with the user's own permissions, so the AI only works with records that person could already open.
We agree on success criteria up front, such as accuracy on a test set of real cases, time saved, and reviewer acceptance, and report against them each release.
Start with an AI readiness assessment
Get a clear view of which AI use cases your data can support today, and what it takes to get the rest ready.