Voice AI is changing how businesses answer calls, qualify leads, and support customers. But building a voice bot that actually works in production takes much more than connecting an AI model to a phone line. In this episode of Ship Happens, host Per Krogslund talks with Seth Wright from CTM (formerly Call Tracking Metrics) about the evolution from call tracking and marketing attribution to configurable telephony platforms powered by AI.
In this episode of Ship Happens, host Per Krogslund talks with Seth Wright from CTM (formerly Call Tracking Metrics) about the evolution from call tracking and marketing attribution to configurable telephony platforms powered by AI.
CTM's platform helps businesses manage calls, texts, routing, call trees, voice bots, and handoffs to human teams. AI adds another layer of intelligence: enriching call data, identifying high-intent leads, triggering follow-ups, and building customer context that helps systems make better decisions about what should happen next.
Seth explains why modern large language models have changed what's possible in voice systems—and why the engineering challenge is far from solved. A voice interaction has to feel immediate. That means minimizing latency, preparing data before it's needed, and designing workflows that can handle real-time decisions such as booking appointments or escalating urgent issues.
The conversation goes beyond the voice interface to explore the work required to make these systems dependable: testing realistic conversations, replaying failures, improving observability, explaining AI-driven decisions, and building evaluation pipelines that catch problems before they reach customers.
Because when a voice AI system is slow, unreliable, or confidently wrong, customers don't care how impressive the demo looked.
Building better voice AI isn't just about making machines talk. It's about engineering systems that listen, understand context, act quickly, and know when to bring in a human.
00:00 — Answer Fast or Lose
01:01 — Meet the Guest
01:33 — What CTM Does
03:54 — AI for Call Insights
05:18 — IVRs and Voice Bots
06:18 — Why Voice Changed
09:35 — Voice Value and Speed
12:03 — Engineering Voice Systems
13:54 — Voice UI Primitives
17:46 — Explainable Lead Scoring
20:54 — Decision Models and Sentiment
23:06 — Model Choices and Latency
27:32 — Real-Time Data Loading
29:09 — Testing and Debugging
33:33 — Evaluation Pipelines and Teams
42:08 — Engineering Craft in AI
44:27 — Advice for New Engineers
48:44 — Future Assumptions
49:55 — Wrap Up and Goodbye
A great voice bot depends on the whole system.
The conversational model is only one part of the experience. Routing, data access, integrations, escalation paths, and operational reliability all matter.
Latency is a product decision.
In a live conversation, even a technically correct response can feel broken if it takes too long. Architecture and data preparation directly shape the customer experience.
Context makes automation more useful.
Enriched call data and customer memory can help systems route conversations, recognize opportunities, and respond more appropriately.
Human handoffs are part of good automation.
Reliable systems need to recognize when a situation calls for human judgment or urgent escalation—not simply keep a conversation going.
AI needs measurable quality controls.
Testing, observability, conversation replay, explainability, and evaluation pipelines help teams identify failures and improve performance before problems compound.
Production engineering is still engineering.
AI can make it easier to build new capabilities, but teams still need to manage complexity, evaluate tradeoffs, maintain reliability, and build systems people can trust.