The problem
Support volume was the visible cost: tickets arriving thin on context, agents spending their time reconstructing what the customer actually meant. Underneath it sat a riskier number — when we measured current usage, 18% of AI tool interactions touched sensitive data, client information moving through tools nobody had approved. Banning tools would have driven usage underground; shipping agents without governance would have scaled the exposure.
What we built
Policy plus architecture, together. Data tiers and approved tools gave people a governed path that was easier than the workaround. On that foundation we deployed production AI agents for both internal and customer-facing use: bots that deflect the questions the knowledge base can answer, and processes that enrich every ticket that does get submitted with the context a human needs to resolve it fast.
Proving the answers were right
Before the agents were trusted with customers, we helped the team build a golden dataset — the vetted questions and answers the system must get right — and stood up evals and benchmarks that run against every change. That's what let the bots take real ticket volume: accuracy was a measured gate, not a hope.
The result
Support tickets submitted dropped 27%, and for the tickets that remained, the context added by the bots and intake processes cut time-to-resolution by 32%. Sensitive-data exposure in AI tools went from 18% of interactions to 0% — not by blocking AI, but by making the governed path the fastest one.