Elevon in ForbesAI First-Line Support That Cut Operator Load by 60%
C-Engineering deployed an AI agent that processes every new support ticket, identifies missing information, searches the knowledge base, and either resolves the ticket automatically or routes it to a live operator, only when the agent is not confident enough to handle it.
Client
C-Engineering
Industry
IT & software
Solution
Custom AI agent (Elevon platform)
Deployment
Production · since Q1 2026
Every ticket reviewed. Missing info requested. Knowledge base searched. Operator involved only when needed.
The Challenge
First-line support is high-volume, repetitive, and uneven in complexity. A significant share of incoming tickets either lack the information needed to process them, or can be resolved with information already in the product documentation. In both cases, a live operator spends time on work that does not require human judgment.
At C-Engineering, support agents handled every ticket from the first message: reading incomplete tickets, writing back to ask for clarification, waiting for responses, then looking up answers in documentation that was already there. The process was slow for customers and inefficient for the team.
The deeper issue was scalability. As ticket volume grew, operator capacity became the ceiling. Adding more agents was the only way to handle more tickets, and most of what those agents were doing could, in principle, be handled without them.
Why Elevon
Completeness check before any reply
Before generating a response, the agent analyses the ticket to see whether everything needed to resolve it is present. If something is missing, it generates a friendly, specific request for that exact piece of information, not a generic follow-up. The customer gets a clear, targeted question rather than silence.
Confidence scoring with KB fallback
After drafting a candidate response, the agent scores its own confidence. Below the threshold, it searches the knowledge base before trying again. This two-stage approach means the agent escalates only when it has genuinely exhausted what it can find, not at the first sign of uncertainty.
Escalation with full context preserved
When the agent cannot resolve a ticket with enough confidence, it routes it to a live operator with the full conversation history and its own analysis intact. The operator does not start from scratch, they see what the agent found, what it tried, and what remains unresolved.
“The agent handles the straightforward tickets and the incomplete ones. The team now focuses on the cases that actually need them.”
The Rollout
The flow runs every 15 minutes and pulls new tickets from the helpdesk. For each ticket it extracts the last message and the conversation history, then determines whether the message needs a response and whether any information is missing. If information is missing, it generates a targeted clarification request and posts it back.
If the ticket can proceed, the agent drafts a response and assigns a confidence score. A score at or above 80 triggers an automatic reply. Below 80, the agent searches the knowledge base. If that raises confidence to the threshold, it replies automatically; if not, the ticket is assigned to a live operator with full context attached.
What this looks like in practice
A customer submits a ticket without their licence version. The agent spots the gap and replies within 15 minutes asking specifically for the version number. The customer responds. The agent processes the updated ticket, drafts a reply at confidence 85, and posts the resolution automatically, the operator never sees it. For a harder query at confidence 65, the agent searches the knowledge base, finds the relevant doc, regenerates at 82, and posts the answer. For an edge case that stays below threshold, the operator receives the ticket with the agent's analysis and attempts already attached.
The suite
Pull
AI agent
Route
Output
Illustrative reconstruction of the production suite.
Support ticket reply
Real output format, recreated with blind sample data.
The Results
Live operator load reduced by 60%, target achieved at launch
Every ticket reviewed within 15 minutes of submission, around the clock
Incomplete tickets handled automatically with targeted clarification requests
Knowledge base search triggered automatically when initial confidence is low
Full conversation history preserved and passed to operators on escalation
Operators receive only tickets the agent has genuinely been unable to resolve
−60%
live operator load
15 min
max time to first review
≥ 80
confidence to auto-resolve
The 60% operator-load reduction was the launch target and was met at deployment. The 15-minute review cadence and the confidence threshold are live configuration values from the production flow.
“Before, every ticket landed on someone's desk. Now the agent handles most of them, and the ones that reach us already have the context we need.”
Why It Worked
AI support tools often fail because they are deployed as a single-stage system: the agent either answers or escalates. That works when tickets are clean and well-formed, but real support queues are not. A significant share of tickets are incomplete, ambiguous, or require information that sits just outside the agent's immediate knowledge.
The C-Engineering flow was designed around that reality. It separates the triage step from the response step, so incomplete tickets are handled as a distinct case before the agent attempts an answer. Confidence scoring adds a second layer: the agent does not guess when uncertain, it searches, and only escalates after that.
The result is a system where each ticket takes the path that matches its actual complexity. Straightforward tickets resolve automatically. Incomplete tickets get targeted follow-up. Ambiguous tickets get a knowledge-base search. Only the genuinely difficult cases reach a live operator, and when they do, the operator has everything the agent collected, so they are not starting from zero.
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