Case Study: How an AI Support Agent Reduced Ticket Volume by 60%

The starting position: support was bleeding $14K a week

The client runs a US-based Shopify Plus brand in the supplements category. Six full-time CS agents on Front, paid blended $52,000/year fully loaded. By mid-2025, ticket volume was averaging 1,900 a week. Cost-per-ticket worked out to $7.20. Most tickets — about 71% in a manual sample we ran — were the same five questions: "where is my order", "I need to change my shipping address", "how do I cancel my subscription", "what is in your blend", and "do you ship to my country".

Their Shopify org had decent data — order, shipment tracking, subscription status — but the agents were copy-pasting from Notion docs and clicking through the admin to read fields. Median response time was 6.1 hours. CSAT was 4.1.

The build: 11 weeks from kickoff to 100% traffic

We scoped the engagement at $68,000 fixed, paid in three milestones. The stack:

• Anthropic Claude Sonnet 4.7 for the conversational layer. Picked Claude over GPT-4o because the Claude prompt-caching pricing dropped per-ticket inference cost from $0.11 to $0.02 once we ran the system prompt as a cached prefix.
• LangChain for tool orchestration. We wrote 7 tools: get_order_status, get_subscription, change_shipping_address, pause_subscription, cancel_subscription, refund_order (capped at $80, escalates above), and lookup_product.
• Shopify Admin API + Recharge API for live data reads. No replication — the agent calls Shopify on every conversation, so the data is never stale.
• Front stayed as the human handoff target. The agent posts to Front via webhook when confidence drops below 0.78 or when a refund above $80 is requested.
• CloudWatch + Datadog for the eval pipeline. Every conversation logged with prompt, tool calls, output, latency, and a 0-1 confidence score from a separate evaluator pass.

The four-phase rollout we ran

1. Weeks 1-3 — Eval set first. Their CS lead labeled 200 historical tickets across 5 categories. We built the eval use BEFORE writing prompts. Every prompt iteration scored against the eval set.
2. Weeks 4-7 — Tool wiring + prompt iteration. 12 prompt versions. Final accuracy on the eval set: 94.3% (correct response + correct tool call + no hallucination on order data).
3. Weeks 8-9 — Shadow mode. Agent ran against every incoming ticket in parallel with the human team for 14 days. We compared agent-suggested response to actual human response. Disagreements went to a daily 30-min review. 18 prompt patches landed in this window.
4. Weeks 10-11 — Gated rollout. 10% → 50% → 100% over 12 days. Hard rollback criteria written before traffic flipped: if CSAT dipped below 4.0 for any 48-hour window, automatic rollback to 0%.

The numbers after 90 days in production

Metric	Before	After
Tickets / week	1,900	763
Cost / ticket	$7.20	$1.40
Median response time	6.1 hours	2.3 minutes
CSAT	4.1	4.7
Annualized CS cost	$524,000	$104,000

The CS team went from 6 FTE to 2. Both remaining agents handle escalations and the daily eval review — higher-skill work at the same pay. No layoffs; the other 4 moved into retention and proactive outreach roles inside the brand.

What we would do differently next time

Two honest misses: (1) we under-invested in the refund-tool guardrails early — the first shadow week showed the agent trying to refund a $340 order, which would have been outside policy. The fix was a hard $80 cap + escalation path, but we should have built that into v1. (2) Confidence calibration was off for the first 30 days of production — too many easy tickets got handed to humans. Recalibrating the confidence threshold from 0.85 to 0.78 increased the automation rate from 56% to 64% without dropping CSAT.

FAQ

Methodology

Numbers come from a single anonymized engagement run between June 2025 and February 2026. Client name withheld under NDA; metrics published with explicit permission. CSAT score is the 5-point post-resolution survey average. Cost-per-ticket includes blended CS salary, software, and infrastructure. AI inference costs use the public Anthropic Claude Sonnet 4.7 pricing as of April 2026. Industry baseline figures (e-commerce AI support ROI 280%) cross-referenced against the Deloitte State of AI 2026 report and the OneReach.ai 2026 benchmark of 5.1-month median payback for customer-service AI deployments.