Financial Services
Enterprise Client (Anonymized) — Consumer Financial Services
A support copilot that cut handle time in half across a 400-agent contact center
42% reduction in average handle time
Deployment · 10 weeks
The situation
A consumer lending division was running a 400-agent contact center handling roughly 38,000 tickets a month — payment disputes, statement requests, account changes, and escalations. Agent attrition was high, ramp time for new hires ran twelve weeks, and leadership had already tried two off-the-shelf "AI support" products that agents quietly stopped using within a month.
The mandate wasn't a chatbot. It was a reduction in cost per ticket that survived contact with real agents and real customers.
The baseline
Before deployment, we spent the first two weeks instrumenting the desktop and the queue, not writing a line of model code.
- Average handle time: 11.4 minutes
- First-contact resolution: 61%
- Cost per ticket: $6.40
- Tier-1 volume eligible for automation (identified from six months of transcripts): 58%
The deployment
Two engineers embedded with the contact center operations team for ten weeks. The system we built sits inside the existing agent desktop — not a separate tab — and does three things: drafts a response grounded in account data and policy documents for every incoming ticket, fully auto-resolves the subset of tier-1 tickets that meet a confidence and policy threshold, and routes everything else to an agent with the draft and reasoning already attached.
It runs against the client's existing CRM and knowledge base. No new systems of record, no migration. Every auto-resolution is logged with the policy citation that justified it, and the threshold for auto-resolution was tuned jointly with the compliance team before go-live.
The outcome
- Average handle time: 11.4 → 6.6 minutes (42% reduction)
- 34% of total ticket volume now resolves without agent involvement, at 96% of agent-level CSAT
- First-contact resolution: 61% → 79%
- Projected annualized savings: $2.1M against the baseline cost-per-ticket
- CSAT: +6 points over the six weeks following go-live
Agents were not reduced. Headcount was redeployed toward the 66% of volume that still requires judgment — disputes, hardship cases, and escalations — where handle time and resolution quality both improved once agents stopped triaging routine tickets manually.
What the client owns now
- The copilot codebase, running in the client's own cloud environment
- The retrieval and grounding pipeline against their knowledge base and policy documents
- The evaluation harness used to tune and monitor auto-resolution accuracy
- Runbooks for adding new ticket categories and adjusting the auto-resolution threshold
CopperPin's engineers rotated off once the client's internal platform team could ship a new ticket category without us in the room.
Client identity, sector specifics, and select figures in this case study have been altered or composited to protect client confidentiality, standard practice for published engagement work. The deployment, methodology, and results described reflect a real CopperPin engagement.