Agentic support triage for a Series-D fintech

The client is a Series-D fintech growing faster than its support organisation could hire, serving a large and expanding customer base across regulated financial products. Support quality is part of the brand promise, and at their stage every percentage point of CSAT feeds directly into retention and reputation.

Much of what their agents handled was routine and answerable from systems the company already ran, yet every ticket still queued for a human. Scaling the team linearly with growth was neither fast enough nor sustainable.

The support queue was overwhelmed with tier-1 tickets, and every one waited on a person even when the answer sat in a system an agent could have queried in seconds. The genuinely hard tickets were buried behind a wall of routine ones.

Wait times stretched and CSAT suffered, not because the team was weak but because human attention was being spent on work that never needed it. Throwing more headcount at the queue only deferred the problem.

Because this was a regulated financial product, any automation had to leave a clean, auditable trail and hand off cleanly to a human the moment a ticket needed real judgement. A black-box bot that resolved tickets without showing its working was never an option.

We built an agentic orchestrator that triages every inbound ticket, resolves the routine ones against live systems within explicit guardrails, and escalates only what genuinely needs a person. The routine majority clears without human touch; the exceptions reach an agent faster.

On every escalation the agent inherits a clean, auditable summary of what the orchestrator saw and did, so the human starts from a diagnosis rather than a blank ticket. Every automated action is logged and replayable for review.

We ran it in shadow mode first, proposing resolutions without sending them, so we could prove its accuracy against the team's own decisions before letting it act, and even then within tightly bounded authority.