Building Policy-Aware AI for Compliance
Why generic LLMs fail in regulated environments, and how Eliot reasons over a firm's internal policies to keep every decision consistent and auditable.
Generic large language models are trained on the public internet. They do not know your firm's AML programme, your risk appetite statement, or which fields are mandatory for a given client type. In compliance, that gap is not a nice-to-have — it is a control failure.
Policy-aware AI means the system reasons over your rules: which documents are required, how ownership thresholds trigger EDD, how to phrase client requests so they align with internal standards. Outputs are traceable to the policies they were grounded in.
Eliot never trains on client data. Models run in environments appropriate to the institution — including private deployment where data does not leave the firm's perimeter. Audit logs and human-in-the-loop review remain first-class.
The result is not "AI that guesses" but automation that executes the same analytical steps analysts would take — faster, at scale, and with consistency that spreadsheets and ad-hoc prompts cannot match.