The AI-native Tier 1 support agent that deflects roughly 40% of incoming tickets and auto-closes about 15% more — production numbers from the Rapax support desk, where Frank has run live since May 2026.

Download the white paper

The deployment plan, the decision logic behind every ticket Frank touches, and how to model the value against your own ticket volume.

Meet Frank in two minutes

Watch the introduction to see how Frank deflects tickets, validates intake, and gives your engineers back their time.

Your support desk isn’t broken — it’s drowning

A large share of support tickets aren’t actually problems. They’re knowledge base questions, incomplete reports, false alarms, and duplicates eating your engineers’ time. On the Rapax support desk, Frank resolves or deflects roughly 55% of incoming volume without a person touching it.

Knowledge base questions disguised as tickets

A substantial portion of incoming tickets could be answered from documentation you already have. Customers ask anyway, and your six-figure engineers spend hours pointing them at pages that already exist.

Incomplete reports and endless back-and-forth

Missing logs. No reproduction steps. Unclear system context. Your engineers spend hours a day asking customers for information that should have been in the ticket from the start.

False alarms at 2 AM

A customer reports an outage. Your monitoring shows everything green. On-call gets paged anyway, loses sleep, and finds out it was a configuration question that could have waited until morning. Frank checks reported outages against live telemetry and downgrades with an explanation — and never silently upgrades, because outage priority carries billing and paging consequences.

The hidden cost of triage

Across a support organization of any size, triage consumes the equivalent of several full-time engineers doing work a machine could absorb. Every new hire buys you time rather than capacity — which is the same problem Rapax solves on the network side, applied to the support queue.

Frank is grounded, not generative

Every decision Frank makes is anchored to Wade, the institutional-memory agent, which returns a confidence label on every query. High confidence deflects or auto-closes. Medium drafts a reply for human review. Low escalates to a person. You set the thresholds. If the answer isn’t in your documentation, Frank doesn’t invent one.

What’s inside the white paper

• The real cost of Tier 1 triage — where the volume actually goes, and how to measure it on your own queue
• How Frank works — the five decisions Frank makes on every ticket, and the confidence thresholds behind each one
• Modeling the value — how to calculate deflection savings against your own ticket volume and loaded engineer cost
• Live use cases — real scenarios from the Rapax production support desk
• Deployment — the platform stands up in five days; the six-week plan covers assessment, integration, knowledge base warm-up, and go-live
• Evaluation and risk mitigation — how the try-and-buy works, no lock-in, full transparency

By Shawn Ennis · Founder, Rapax — a Citus Technologies company
May 2026 · 12 pages

Evaluate Frank on your own desk

Rapax deploys in five days. You evaluate for thirty against success criteria you define in writing before anything is signed, for $25,000 — credited against the license if you move forward. If it doesn’t meet them, the evaluation ends and you owe nothing further.

Skip the reading. Book fifteen minutes and we’ll walk you through the live deflection dashboard from our own production support desk. Book 15 minutes →