Auto-resolution from 0% to 55% in 9 months.
A PE-backed managed services operation had every ticket, from password resets to complex configurations, landing in the same queue for a human. KeyDelta deployed an AI virtual agent that autonomously resolves 55% of tickets while escalating frustrated or complex cases with full context. Autonomous resolution held because the operating model came first: clear decision rights on what the agent resolves versus escalates, ownership of edge cases, and a cadence that caught bad resolutions in hours, not months. Operator-Built AI, built on a fixed operating model, not bolted onto chaos.
Situation
A PE-backed managed services operation was fielding 3,000+ IT support tickets and emails per month for a flagship product. Every ticket, from password resets to complex configuration issues, landed in the same queue and waited for a human. Staffing costs were climbing, wait times were growing, and customer satisfaction was slipping as the business scaled. The team needed to handle more volume without proportionally more headcount.
- 3,000+ monthly tickets for a single product line, all requiring human triage and response
- Simple issues (password resets, status checks, how-to questions) consuming 40%+ of agent time
- Average first-response time of 4+ hours during peak periods, customers escalating out of frustration
- No sentiment awareness, frustrated customers got the same queue position as routine inquiries
- Support team scaling linearly with ticket volume, every 500 new tickets required another FTE
Approach
KeyDelta built an AI virtual agent that sat at the front of the support queue, resolving what it could, enriching what it couldn't, and intelligently routing everything else.
- 01
Contact suite integration
Deployed on Evolve Contact Suite with a .NET backend. The virtual agent intercepted inbound tickets and emails natively within the existing support workflow, no new tools for customers or agents to learn.
- 02
Knowledge & resolution engine
Trained on the product's knowledge base, past ticket resolutions, and support documentation. The agent could diagnose common issues, walk customers through solutions, and execute routine actions like password resets and configuration checks autonomously.
- 03
Sentiment analysis & smart escalation
Rolling-window sentiment analysis tracked customer tone across the full conversation trajectory, not just per-message classification. Escalation triggers fired on negative sentiment trends, multi-step complexity, and out-of-scope detection. Human agents received full context, sentiment trajectory, and a recommended resolution.
- 04
Voice channel extension
LiveKit and Broadworks integration extended the virtual agent to voice channels. Customers calling in got the same AI-first experience, with a clean handoff to human agents when needed, preserving full conversation context across channels.
Stack
Outcome
Over nine months, the virtual agent went from resolving none of the queue to 55% of tickets, with 91% CSAT on AI-resolved tickets against an 89% human baseline. First response fell from 4.2 hours to 2.2, support cost dropped from $18 per ticket to $11.80, and agent satisfaction rose from 3.1 to 4.4 out of 5. Builds like this now ship under the KeyDelta Method and stay current under AAMP.
| Measure | Before | After | Change |
|---|---|---|---|
| Auto-resolution. Over half resolved by AI | 0% | 55% | 55% |
| First response. 47% faster for humans too | 4.2 hrs | 2.2 hrs | −47% |
| CSAT (AI tickets). Matching human baseline (89%) | N/A | 91% | 91% |
| Support cost. 34% reduction per ticket | $18/ticket | $11.80 | −34% |
| Escalation quality. Sentiment + history | Blind | Enriched | Full context |
| Agent satisfaction. Meaningful work, not resets | 3.1 | 4.4 / 5 | +42% |
“We were hiring a new support agent for every 500 new tickets. Now the AI handles the straightforward stuff better than we did, 91% CSAT on AI-resolved tickets, matching our best humans. Support costs dropped 34%. But here's what surprised us: agent satisfaction jumped from 3.1 to 4.4 because they stopped doing password resets and started solving real problems. Customers happier, team happier, costs down. That's the math every CEO wants to see.”