B2B SaaS · Customer Operations
Composite engagementCutting tier-1 ticket volume 71% at a Series-B SaaS
Replaced first-touch human triage with an agent that resolves billing, account, and integration questions, escalating only what genuinely needs a human.
Client type
Series-B SaaS
Size
~140 employees, $25M ARR
Region
North America
The problem
Customer Ops was drowning in tickets after a successful product expansion. 60% of inbound was billing, account, and integration questions that didn't need a human, but the team was spending 80% of their hours on them. Hiring couldn't scale fast enough, and CSAT was sliding from 4.7 to 4.2.
Approach
- 01
Pulled 6 months of ticket transcripts; clustered by intent and identified the top 22 resolvable patterns
- 02
Built a retrieval-augmented agent with ground truth from the help center, billing system, and product docs
- 03
Wired tools for refund processing, plan changes, integration token rotation, and seat management. All with allowlists and human-approval gates on destructive actions
- 04
Stood up a Langfuse-based eval suite running against 500 historical tickets on every code change
- 05
Rolled out behind feature flag to 10% of traffic, then 50%, then 100% over 3 weeks based on eval data
Architecture
Outcomes
"We had three vendors quote this. Cloptim was the only one who started with our eval strategy and ticket data, not the model. That's why we picked them, and that's why it shipped."
Timeline
- Week 1
Discovery: ticket clustering, ROI model
- Week 2
Design sign-off, eval suite spec
- Weeks 3-4
Agent v1 + tool integrations
- Week 5
Eval suite live, internal beta on 10% traffic
- Week 6
Rolled to 50%, then 100% production
Tech notes
- ▸Anthropic Claude Sonnet 4 for reasoning + drafting
- ▸pgvector for retrieval (existing Postgres saved migration cost)
- ▸Langfuse for eval + trace dashboards
- ▸Approval queue built into existing Zendesk side-conversation UI
- ▸Feature flag rollout via LaunchDarkly
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