The challenge
A major U.S. financial planning, tax and accounting firm had spent a year and a half trying, and failing, to get its own machine-learning agents into production. Manual work slowed auditing, client advice and information retrieval, and there was no governance, security or validation framework for sensitive financial data, which meant a real risk of compliance breaches.
Our approach
We deployed five production-grade AI agents with secure access to market and client data, on a full MLOps foundation for deployment, versioning and continuous delivery. An AI gateway monitors cost, enforces compliance guardrails and detects data drift in real time, and automated benchmarking and feedback loops keep quality improving.
The outcome
After 18 months of stalled investment, the firm’s AI is in production. Productivity in auditing, advice and information access rose eightfold, saving $500K to $1M a year, and client response times improved by 20% to 30%. Every agent runs with full data tracing, guardrails and cost control, on a foundation built to add more.
a year in savings, up to $1M
faster client response
AI agents in production
