Retrieval-Grounded AI Automation for an Enterprise Support Operation

The client's support operation was losing a volume race. More than 30,000 tickets arrived every month across a large enterprise product surface, and the queue's arithmetic was unforgiving: first response averaged 14 hours, hiring could not keep pace with ticket growth, and the most experienced agents spent their days on the most repetitive questions — password policies, configuration steps, licence entitlements — rather than the genuinely hard cases.

Leadership had evaluated off-the-shelf AI deflection tools and rejected them for a reason that matters in enterprise support: they could not tolerate a system that invents answers. A wrong-but-confident response about a security setting or a billing entitlement is worse than a slow human one. Any automation would have to be verifiably grounded in the client's actual documentation, know when it did not know, and hand off cleanly to humans — with evidence, not vibes, that it met that bar before customers ever saw it.

We built a retrieval-grounded agent over the client's documentation with strict citations and confidence-gated escalation, integrated through a typed tool layer for scoped account actions, and validated it in shadow mode against 20,000 historical tickets before it answered a single live customer.

Grounded answers or no answer. Every response is composed from passages retrieved from the client's documentation, release notes, and internal knowledge base, and every claim carries a citation back to its source. If retrieval cannot support an answer with sufficient confidence, the agent does not improvise — it escalates. Hallucination is treated as a defect class with a budget of zero, enforced by evaluation rather than hoped for.

Confidence-gated escalation. Escalation thresholds are tuned per ticket category: the agent resolves routine configuration and how-to tickets autonomously, while anything touching security, billing disputes, or ambiguous intent routes to a human — accompanied by the agent's retrieval results and a structured summary, so the human starts warm instead of cold.

A typed tool layer for actions. Where resolving a ticket requires doing something — checking an entitlement, resending an invitation, resetting an integration — the agent acts through a typed API layer with strictly scoped, least-privilege permissions and full audit logging. The agent cannot perform any action outside its declared tool contracts, which made the security review a review of a finite list rather than an open question.

Shadow mode before production. For eight weeks the system ran in shadow, answering 20,000 historical and live-mirrored tickets invisibly while its responses were scored against what human agents actually did. Category-level accuracy from shadow mode determined the rollout order; nothing was automated on trust. Dashboards in Grafana track resolution quality, escalation rates, and citation coverage continuously in production.

The agent now auto-resolves 61% of ticket volume end to end — the routine majority that previously consumed most of the team's capacity. First response fell from 14 hours to under 2 minutes for every ticket, because even escalated cases receive an immediate grounded acknowledgement and reach a human with context attached. Cost per ticket dropped 52%, letting the support organization absorb continued ticket growth without headcount growth.

Two secondary outcomes stand out. Blind quality reviews score auto-resolved tickets at parity with the human baseline — the escalation gate means the agent simply declines the cases it would get wrong. And the human team's work has shifted up-stack: senior agents now spend their time on complex product issues and on curating the documentation the agent retrieves from, which improves both channels at once.

"It handles the volume we could never hire for, and it does it without making things up."

— Hannah Brückner, Director of Customer Operations

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