Hey Zendesk Community,
I'm building Teravictus - a churn prediction and quality monitoring layer for support teams. We sit on top of your existing Zendesk setup and analyze ALL support interactions (bot and human) to flag customers at high risk of churning before they leave.
Why this exists:
I spent the last week doing deep competitive analysis across AI support tools - Zendesk AI, Fin AI, Pylon, and others. What I found in published research helped me understand a challenge many support teams are facing:
The research data:
- 21% of AI chatbot "resolutions" actually resolve nothing - customers abandon frustrated (ASAPP Research - proprietary study)
- 96% of churning customers never complain - they just silently leave (Mixpanel & thinkJar Research)
- 17% resolution rate for complex issues like billing disputes despite high containment rates (Gartner Customer Service Survey, 2023)
The challenge I hear from support leaders:
AI chatbots help with L0/L1 automation - password resets, basic FAQs. That works great for maybe 20-30% of tickets. But here's what keeps coming up:
- You're getting pressure to automate more, but you're not sure if the bots are actually helping or frustrating customers
- Your human agents still handle 70-80% of tickets (complex L2/L3 issues), and you have no real visibility into whether those interactions are creating churn risk
- You're measured on metrics like containment rate and response time, but you don't actually know if customers are happy or about to leave
- By the time CSAT surveys come back, it's often too late
What Teravictus does:
We monitor EVERY support interaction - whether handled by AI or humans - and predict which customers will churn based on support quality. 92% accuracy. Real-time Slack alerts when someone's at risk.
We work with teams who:
- Don't fully trust autonomous AI yet and want oversight
- Already have chatbots but need monitoring on human support too
- Want churn prevention at $25/week vs. expensive per-agent AI costs
Why I'm posting:
Before I take this positioning to market, I want feedback from people running actual support operations:
- Does this challenge resonate with your experience? Is there really a blind spot in monitoring human support quality?
- The "trust gap" with AI - is this real? Are teams hesitant about relying more on autonomous chatbots, or is that concern overblown?
- Churn prediction from support quality - do you measure this today? Or is it something you wish you could see?
- What am I missing? What obvious objection would a support leader have that I'm not addressing?
Happy to share the full competitive analysis research document (with complete citations and sources) if anyone wants the detailed breakdown.
Thanks for any feedback - especially if you think I'm wrong about the problem.
Vishnu
Founder, Teravictus
www.teravictus.com