Prosights installed an AI operating layer over the firm's existing ledgers: it extracts every field with a citation to the exact spot on the source, applies the client's COA memory, and clears the routine majority under stated policy while routing only genuine exceptions to a human. Each correction is saved as a versioned, client-scoped rule and applied to the next batch — so auto-approval compounds close over close.
The Challenge
Outsourced bookkeeping at scale meant manual data entry and per-client COA coding across countries and systems — high cost to serve, and judgment that disappeared after every close, turning each similar transaction into another review.
The Approach
Not another tool in the stack — an operating layer across the existing ledgers. AI absorbs the document volume; a policy gate (thresholds, sum checks, codes restricted to the client's own COA) decides where human judgment is spent; everything released is logged with evidence.
The Build
Extraction with cell-level citations boxed on the source PDF; a client-scoped COA memory that versions every coding decision; adoption inside Excel and a simple web app with no firm-wide UI rollout; and a KPI layer tracking auto-approval rate, reviewer hours returned, exception mix, and decision reuse.
The Outcome
~1,000 documents cleared to ~6 human decisions per batch, ~95% steady-state auto-approval, 100% of intended users live within a week — and a governed, exportable memory the firm owns. Judgment compounds instead of evaporating.
