Subvolts take Coxwave's Align offers outcome focused AI evaluation beyond accuracy, measuring satisfaction and task completion. Oct 26, 2025 - Developers
- Learn how to design an evaluation stack that guides prompts, agents, and UX, and track experiments with model and prompt change logs.
- Business
- Discover how to move beyond accuracy metrics to measure user satisfaction and task completion, preventing adoption blockers.
This interview provides practical insights into advanced AI evaluation techniques from a company developing specialized tools for product teams.
#296 Yeop Lee: How Coxwave is Redefining AI Evaluation
Audio streams from Craig S. Smith. Your place is remembered on this device.
From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Eye on AI episode #296 features Yeop Lee of Coxwave discussing how their Align product moves AI evaluation beyond simple accuracy. They explore measuring satisfaction, trust, and task completion across various communication channels. The conversation covers pairing AI judges with human review, identifying hidden failure patterns, and building an evaluation stack to guide AI development and deployment.
This episode explains how companies can better test their AI tools. Instead of just checking if the AI is 'correct,' they look at whether people are happy, trust the AI, and actually finish their tasks. This helps make AI more useful in everyday conversations and services.
This episode offers product teams and AI developers insights into comprehensive AI evaluation. It details methods for measuring user satisfaction and trust, not just accuracy, which is crucial as frontier labs like Google Gemini and OpenAI refine their models for real-world applications. Understanding how to identify and fix adoption blockers through conversation analysis and experiment tracking helps ensure AI products are effective and user-friendly, a key challenge for all major AI players.
- HostCraig S. Smith
- GuestYeop LeeHead of Product, Coxwave
- Episode Title: #296 Yeop Lee: How Coxwave is Redefining AI Evaluation
- Show: Eye on AI
- Guest: Yeop Lee, Head of Product at Coxwave
- Length: 43 minutes
- Evaluation Focus: Outcome focused evaluation with Coxwave's Align, measuring satisfaction, trust, and task completion.
- Key Metrics: Completion rate, CSAT, retention, and cost per resolution.
This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.
Visit https://agntcy.org/ and add your support. How is Coxwave Redefining AI Evaluation?
In this episode of Eye on AI, host Craig Smith is joined by Yeop Lee, Head of Product at Coxwave. Together they explore how teams move beyond accuracy-only metrics to outcome focused evaluation with Coxwave's Align. We look at how Align measures satisfaction, trust, and task completion across chat, email, and voice, how LLM as judge pairs with human review, and how product teams search conversations to find hidden failure patterns that block adoption.
Learn how leading companies design an evaluation stack that guides prompts, agents, and UX, which pitfalls to avoid when shipping updates, and which metrics matter most for success, including completion rate, CSAT, retention, and cost per resolution. You will also hear how to run experiment tracking with model and prompt change logs, set up governance that prevents regressions, and choose between SaaS and on premise deployments that meet security and compliance needs.
Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI
- LLM as judge
- Yeop Lee
- Using AI models to help evaluate other AI outputs.
- CSAT
- Yeop Lee
- Customer Satisfaction, a measure of how happy customers are with a product or service.
55 min42 min51 min34 minUkraine's Secret Weapon: The Points System Winning the Drone War | Andrii Hrytseniuk
WithAndrii Hrytseniuk
Questions people ask
- How does Coxwave's Align evaluate AI?
- Align moves beyond accuracy-only metrics to focus on outcomes like satisfaction, trust, and task completion across chat, email, and voice.
- What metrics matter for AI success?
- Key metrics include completion rate, customer satisfaction (CSAT), retention, and cost per resolution.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Craig S. Smith.