Before state
Client evidence and instructions were split across systems, making renewal, MTA and subjectivity checks slower and harder to evidence during file review.
Case studies
Custom AI agent case studies and implementation outcomes
Anonymized implementation models for document-heavy teams: the before state, the controlled AI workflow, the review points, and the operational metrics used to judge whether the build is worth scaling.
Published model
AI-assisted client file control for commercial insurance brokerage
A fixed-scope model for a 50-person broking team working across BMS records, Outlook, Teams, SharePoint, OneDrive, insurer portals, PPL records, PDFs and spreadsheets.
Controlled workflow
The proposed workflow classifies documents, matches them to client and policy records, flags missing evidence, routes exceptions, and keeps staff approval before system updates.
Measured result
Target metrics include 15 minutes saved per user per day, 25% fewer evidence queries, 30% fewer missing-evidence review issues, and payback under six months.
What each case shows
Source documents, existing systems, manual checks, exception paths, human review gates, and where AI is allowed to act.
How outcomes are measured
Time saved, exception reduction, reviewer confidence, audit traceability, correction rates, and whether the workflow is stable enough to expand.
Where to compare workflow candidates
Use the broader workflow library to match document intake, extraction, validation, review and integration patterns to your own operation.
Explore workflow library