A DTC e-commerce brand doing $18M/year was drowning in support tickets. Volume had tripled in a year, response times had ballooned to 14 hours, and they were about to hire four more agents. Instead, we deployed an AI support agent. Here's exactly what we built and what it delivered.

Results after 90 days
71%
tickets auto-resolved
no human touch
-62%
support cost per ticket
14h → 2m
first response time
+11pts
CSAT improvement
84 → 95

The problem

Rapid growth had broken the support function. The team was answering the same questions hundreds of times a day — where's my order, how do I return this, does this ship to my country — while genuinely complex issues waited in the same queue. Customers were frustrated, agents were burning out, and the proposed fix (hiring) only scaled the cost, not the experience.

The support function, before and after

Before Mintzoro
  • 14-hour average first response time
  • 5 agents drowning in repetitive tickets
  • CSAT slipping to 84 and falling
  • Plan to hire 4 more agents (+$220k/yr)
  • No coverage nights or weekends
After Mintzoro
  • 2-minute average first response time
  • 71% of tickets resolved with zero human touch
  • CSAT up to 95
  • Hiring plan cancelled — team reassigned to retention
  • 24/7 coverage in 6 languages

What we built

We deployed an AI support agent grounded in the brand's real knowledge base, order system, and returns policy. The agent could act, not just answer — it looked up live order status, initiated returns, and applied policy-compliant resolutions, escalating anything sensitive or low-confidence to a human with full context attached.

  • Grounded knowledge — connected to help docs, policies, and product data so answers are always current.
  • Real actions — live order lookups, return initiation, address changes via secure tool calls.
  • Smart escalation — anything emotional, high-value, or low-confidence routed to a human instantly.
  • Full transcripts — every conversation logged and reviewable for quality and training.
A support team reviewing dashboards together
Agents shifted from answering 'where is my order' to high-value retention and VIP conversations.

How we rolled it out

From kickoff to full deployment in 6 weeks

  1. 1
    Week 15 days
    Discovery & ticket analysis

    Analyzed 12 months of tickets to find the top intents and map which were safe to automate.

  2. 2
    Week 2–310 days
    Build & grounding

    Connected the knowledge base and order system, defined tools, and set escalation guardrails.

  3. 3
    Week 45 days
    Shadow mode

    Agent drafted responses for human review only — we measured accuracy before it ever replied live.

  4. 4
    Week 55 days
    Gradual go-live

    Enabled auto-resolution for the highest-confidence intents first, expanding as accuracy held.

  5. 5
    Week 65 days
    Full deployment & tuning

    Rolled out across all channels and languages with a weekly review loop for continuous improvement.

Why shadow mode mattered
Running the agent in draft-only mode for a week let us prove 90%+ response accuracy before a single customer saw an AI reply. It turned a risky launch into a measured one.

The return on investment

12-month ROI
Investment
$48,000
Return
$406,000
746%
ROI · first 12 months

Combines cancelled hires, reduced cost-per-ticket, and retained revenue from faster resolution.

"We expected a chatbot. We got something that genuinely resolves problems — and our CSAT went *up*, not down. It paid for itself in the first month and freed our team to focus on the customers who actually need a human."
PN
Priya Nair
VP of Customer Experience, DTC e-commerce brand

Key takeaways

What made this work

  • Grounded the agent in real, current data — no hallucinated policies
  • Gave it the ability to act, not just answer
  • Proved accuracy in shadow mode before going live
  • Escalated sensitive cases to humans with full context
  • Measured everything against a clear cost baseline

Frequently asked questions