Cutting Operational Costs by 40% with LLM Workflows
A step-by-step breakdown of how we replaced a 6-person support team with a single intelligent AI pipeline — without losing quality.
Mihhail
Project Manager, Nordic AI
When a mid-sized SaaS company came to us with a €40k monthly support bill and a 72-hour average response time, the problem was obvious. Six support agents, handling tickets manually, working through a Zendesk queue with no intelligent routing, no automated responses, and no pattern recognition. We replaced most of that with a single LLM workflow.
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The Problem With Traditional Support Scaling
Support is one of the few business functions where adding headcount adds cost linearly but does not improve quality proportionally. The sixth agent does not serve customers six times better than the first. They handle more volume — but make the same types of mistakes, have the same knowledge gaps, and burn out at the same rate.
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How We Structured the LLM Pipeline
The pipeline we built had five stages: intake classification, intent resolution, knowledge retrieval, response generation, and quality scoring. Each ticket enters the pipeline, is classified by type and urgency, matched against a RAG knowledge base built from their documentation, and a response is drafted. The quality scorer then decides whether to send automatically or route to a human.
- Stage 1: Multi-class ticket classification (bug, billing, feature, onboarding)
- Stage 2: Intent extraction and urgency scoring
- Stage 3: RAG retrieval from product docs, past tickets, and runbooks
- Stage 4: Response generation with tone matching to customer history
- Stage 5: Confidence scoring — auto-send or human review queue
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Results After 60 Days
After two months in production, 78% of tickets were resolved without human intervention. Average response time dropped from 72 hours to 11 minutes. Customer satisfaction scores increased from 3.8 to 4.6 out of 5. The support team headcount went from six to two — both now focused on complex escalations and relationship management rather than repetitive ticket resolution.
The takeaway
The goal was never to eliminate the support team. It was to eliminate the waste — the repetitive, low-skill work that drained talented people and delayed customers. The two remaining agents now do more valuable work and are more engaged. The company saves €28k per month. Both are winning.
