How I AIHow to turn meeting notes into prototypes that your sales team can immediately demo to customers
CHAPTERS
Meet Anjan (CPTO) and the core promise: faster alignment with AI prototypes
Claire Vo introduces Anjan Panneer Selvam (CPTO of Acolyte Health) and frames the episode around a new operating model: using AI to turn stakeholder conversations into interactive prototypes that sales and customers can use immediately. The emphasis is not just speed, but reducing friction across product, engineering, and go-to-market teams.
How AI blurs product and engineering boundaries (and why CPTOs thrive)
Anjan explains how AI reduces the “translation cost” between ideas, product intent, and engineering execution—making the combined CPTO role more practical. He argues AI shifts focus away from process overhead toward making possibilities tangible quickly.
Stakeholder idea → prototype workflow: brain dump to structured build prompt
The workflow begins in a live stakeholder meeting (often with a CEO request like “build a journey builder”). Anjan captures rough natural-language input, uses ChatGPT to normalize it into a structured prompt, then feeds that prompt into prototyping tools like Lovable or v0 to generate an interactive experience.
Capturing meeting transcripts with the Limitless Pendant for instant AI prompting
Anjan demonstrates using the Limitless Pendant wearable to record meetings and generate transcripts with minimal friction. Those transcripts become immediate inputs to ChatGPT—turning spontaneous stakeholder conversations into prototype-building prompts.
Building an interactive workflow/journey builder prototype in Lovable (with React Flow)
With the structured prompt, Anjan builds a high-fidelity interactive prototype in Lovable—enhanced by specifying implementation details like React Flow for a canvas-based builder. He iterates progressively, starting simple and layering complexity, until the prototype supports drag-and-drop journey construction.
Why prototypes beat documentation for complex logic-heavy products
Claire and Anjan argue interactive prototypes are especially powerful for complicated rule builders where documentation becomes unreadable. Clicking through behavior exposes edge cases more naturally than reading long PRDs, enabling cheaper failure and faster correction with executives and engineering.
Market research in minutes: Perplexity deep research + ‘devil’s advocate’ prompting
After prototyping, Anjan validates demand and monetization potential using Perplexity for fast market/competitive research. Because AI can be overly optimistic, he explicitly asks for pushback and counterarguments to decide what to build—and what to reject—faster.
Turning research into stakeholder-ready decks with Gamma
Anjan converts the validated concept and research into polished slides using Gamma, making it easier to communicate strategy to execs and cross-functional partners. The deck helps signal preparedness and creates a shared narrative around value, differentiation, and next steps.
ChatPRD as the requirements gate: validating details before engineering execution
To handle inevitable follow-up questions (“what exactly do you mean?”), Anjan uses ChatPRD to pressure-test requirements. He relies on its structured validations, aligns a repeatable PRD template with his team, and produces concise, readable specs with a clear v0/v1/v2 framing.
A living demo/prototype library for sales and customer success to use with customers
Anjan describes deploying prototypes as microsites that mirror the product’s design language, creating a “living product library.” Sales and customer success can demo these reliably (unlike brittle Figma clickthroughs), gather real-time feedback, and accelerate customer alignment without engineering interruptions.
Breaking engineering deadlocks with mobile prototypes using Rork (no mobile dev needed)
When engineering resists an idea due to missing mobile expertise, Anjan uses Rork to prototype a simple mobile app (e.g., capturing selfies for avatar expressions) to prove feasibility. The intent isn’t to replace engineering, but to unblock uncertainty and reframe the conversation around what it would take to productionize.
Operating model takeaways: AI elevates PMs and speeds org-wide alignment
They zoom out to the organizational implications: companies will spend more time upfront validating ideas, aligning teams, and training support/sales earlier—before production build. AI doesn’t remove the need for strong product sense; it shifts PMs toward outcomes, precision, and faster iteration cycles.
When AI outputs aren’t good: step back, switch tools, and iterate smarter
Anjan acknowledges failure cases: many prototype attempts don’t work on the first try. His strategy is to pause, change approach (often returning to ChatGPT to clarify), and avoid endless iteration inside a single tool—balancing patience with practicality.
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