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Building AI Products At Enterprise Scale
In this episode, host Amir Khan speaks with Joe Delgado, Co-Founder and CTO of ChatRank, about building reliable AI products that move from demo stage to production scale. They discuss hallucinations, evaluation systems, product market fit in AI, human in the loop design, enterprise trust, cost constraints, and how teams can build scalable systems that work in real-world environments while balancing speed, accuracy, and long-term product reliability.
PureLogics Pulse Episode Chapters
00:00 – 01:10 | Opening: AI Beyond Demos
The episode opens with a discussion on why building AI is not just about prototypes but about production reliability, trust, and handling real-world constraints in scalable systems.
01:10 – 03:00 | Podcast Welcome and Guest Introduction
Amir Khan introduces Joe Delgado, Co-Founder and CTO of ChatRank, and sets the stage for a deep dive into AI product challenges and real-world deployment.
03:00 – 07:00 | ChatRank and AI Visibility Problem
Joe explains ChatRank’s mission to help brands understand visibility in AI search systems and improve how they appear across emerging AI-driven platforms.
07:00 – 11:30 | AI Product Market Fit Challenges
The discussion explores how AI product market fit differs from traditional software, especially due to rapid prototyping and misleading demo performance.
11:30 – 16:30 | Hallucinations and AI Reliability
Joe breaks down hallucinations, from obvious errors to subtle failures, and explains why evaluation systems and structured testing are essential.
16:30 – 21:00 | Evaluation Systems and Feedback Loops
The conversation highlights synchronous and asynchronous evals, model grading systems, and layered feedback loops to improve AI accuracy.
21:00 – 26:00 | Human in the Loop and Enterprise Trust
Joe explains why human review remains critical for brand safety, enterprise trust, and ensuring AI outputs align with business goals.
26:00 – 30:00 | Cost, Scaling, and Unit Economics
The discussion covers compute costs, margins, and why AI startups must carefully manage economics while scaling products.
30:00 – 33:30 | Conclusion: Building Responsible AI Systems
The episode closes with insights on avoiding hype driven development and focusing on real user problems, reliability, and sustainable AI product design.
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By PureLogicsIn this episode, host Amir Khan speaks with Joe Delgado, Co-Founder and CTO of ChatRank, about building reliable AI products that move from demo stage to production scale. They discuss hallucinations, evaluation systems, product market fit in AI, human in the loop design, enterprise trust, cost constraints, and how teams can build scalable systems that work in real-world environments while balancing speed, accuracy, and long-term product reliability.
PureLogics Pulse Episode Chapters
00:00 – 01:10 | Opening: AI Beyond Demos
The episode opens with a discussion on why building AI is not just about prototypes but about production reliability, trust, and handling real-world constraints in scalable systems.
01:10 – 03:00 | Podcast Welcome and Guest Introduction
Amir Khan introduces Joe Delgado, Co-Founder and CTO of ChatRank, and sets the stage for a deep dive into AI product challenges and real-world deployment.
03:00 – 07:00 | ChatRank and AI Visibility Problem
Joe explains ChatRank’s mission to help brands understand visibility in AI search systems and improve how they appear across emerging AI-driven platforms.
07:00 – 11:30 | AI Product Market Fit Challenges
The discussion explores how AI product market fit differs from traditional software, especially due to rapid prototyping and misleading demo performance.
11:30 – 16:30 | Hallucinations and AI Reliability
Joe breaks down hallucinations, from obvious errors to subtle failures, and explains why evaluation systems and structured testing are essential.
16:30 – 21:00 | Evaluation Systems and Feedback Loops
The conversation highlights synchronous and asynchronous evals, model grading systems, and layered feedback loops to improve AI accuracy.
21:00 – 26:00 | Human in the Loop and Enterprise Trust
Joe explains why human review remains critical for brand safety, enterprise trust, and ensuring AI outputs align with business goals.
26:00 – 30:00 | Cost, Scaling, and Unit Economics
The discussion covers compute costs, margins, and why AI startups must carefully manage economics while scaling products.
30:00 – 33:30 | Conclusion: Building Responsible AI Systems
The episode closes with insights on avoiding hype driven development and focusing on real user problems, reliability, and sustainable AI product design.