Mejix · AI Customer Support
Reduce Support Costs by
30–40% Without Sacrificing Quality
An AI-powered support operating model for enterprise teams.
Integrated with your stack. Owned by your team.
02 / 08 · The Problem
Volume up. Budget down.
Every US support leader is being asked to handle more tickets with the same — or smaller — team.
+25–30%
Ticket volume YoY
Post-pandemic demand keeps climbing.
+18–22%
Support labor cost
Wages, benefits, and remote infrastructure.
$25–40
Cost per ticket
Industry-typical range across US support orgs.
40%
Annual agent turnover
Burnout from overtime and rising case loads.
The dilemma
Every option costs more — except the right one.
- Hire more agents → +30–40% labor cost.
- Hold the line → burnout, churn, CSAT drops.
03 / 08 · The Cost of Inaction
The real cost of doing nothing.
One scenario — a typical mid-size SaaS support org with 80,000 tickets per year.
Today
80,000 tickets · 15 agents
$2.4M
Annual support cost
$30 per ticket · ~$1.2M loaded team cost.
If you do nothing
Add 18–20 agents to keep pace
+$300K
Annual increase
More salary, software, overhead — same cost-per-ticket.
With AI support model
40% routine handled by AI · 60% to agents
$200–300K
Year 1 savings
Same headcount, 40% more volume handled.
ROI math
Pays back in 3–5 months.
Investment
$80–120K
Year 2+ savings
$200–300K / yr
04 / 08 · Why Off-the-Shelf Fails
Why ChatGPT, Zendesk bots, and DIY AI fall short.
Standard chatbot solutions create the illusion of automation — and the reality of escalations.
Disconnected from your systems
Bots can't see Salesforce history, your knowledge base, product docs, or pricing rules — agents waste time re-entering context.
Can't handle your business logic
No grasp of refund policies, billing rules, or SLAs. Escalates the easy stuff and resolves the risky stuff.
Quality issues
Hallucinations, off-brand tone, can't chain steps (order status → refund → billing) without breaking.
Compliance & security risk
PII handling, CCPA/HIPAA exposure, no audit trail of where customer data actually goes.
The result
70–80% AI-to-human escalation — defeating the entire purpose.
Customers
Frustrated — bot doesn't know their history.
Agents
Re-doing work the bot couldn't finish.
Savings
~20% of what was promised.
05 / 08 · The Operating Model
Integrated, compliant,
and measurable.
Three phases — pilot, codify, scale. Built into your stack, sized to your team, owned by you.
Pilot one team
Start with one team or one ticket category. Capture baselines: response time, resolution rate, CSAT. Deploy AI agent assist.
Document what works
Define success criteria, edge cases, and escalation rules. Train agents to work with AI. Build the playbook your team owns.
Scale to more teams
Apply the playbook across teams. Each rollout costs less. Cumulative impact: lower cost per ticket, higher volume handled.
Key principles
06 / 08 · Real Proof
B2B SaaS · 32% cost reduction.
Six-month engagement. One pilot team, then scaled. CFO had said no to new headcount.
- Industry
- B2B SaaS — customer data platform
- Volume
- 120,000 tickets / year
- Team
- 20 agents · 2 managers
- Pilot
- Technical support team (6 agents) with AI agent assist
Average response time
−57%
First-contact resolution
+19 pts
Cost per ticket
−32%
Customer satisfaction (of 5)
+0.5
Year 1
~$380K saved
07 / 08 · Sizing
Size this to your support operation.
Same operating model. Three pre-shaped engagements — flexed to your ticket volume and team.
Small
20–40K tickets / yr
4–6 agents · 2 months
Medium
50–100K tickets / yr
10–15 agents · 3 months
Large
150K+ tickets / yr
25+ agents · 4–5 months
Quick calc
Current spend
Tickets / yr × $25–35
Annual savings
Current spend × 30%
Example
80K × $30 → $720K saved
08 / 08 · Common questions
Quick answers.
The questions support and CX leaders ask before they greenlight an AI pilot.