“Give the Model a Layup”: How to Cut AI Cost Without Cutting Quality | Arjun Singh Jamwal (Verizon)

October 27th, 2026  |  Season 2  |  28 mins 36 secs

business, digital experience, product management, technology

When AI does the heavy lifting inside your product, it changes the historic economics of software: you pay for that work every time a user touches it. For a funded team, that’s a line item. For a bootstrapped project, it can destroy the business.

Arjun Singh Jamwal is a Senior PM at Verizon, where he’s worked on Verizon Cloud and now Verizon Home. On the side, he’s bootstrapping Goodmuncher, an app that helps people eat fewer ultra-processed foods, so every token comes out of his own pocket.

His solution is to give the model a layup, as he puts it. Instead of having AI analyze every ingredient from scratch each time a user scans a label, Goodmuncher scores ingredients and products ahead of time and stores embeddings in a vector database. When a user looks something up, the model finds the answer that was already processed instead of analyzing from scratch each time. Same result for the user, a fraction of the inference cost for the company.

In this episode, Arjun gives a live demo of that approach with a pipeline that turns a single command into a 10-slide social carousel in about 30 seconds. Along the way, he shares:

• How better upfront prep lets smaller, cheaper options perform like frontier models and makes swapping models easier down the line
• Why he hard-codes the rules he can’t break instead of trusting the model to remember them
• His step-by-step framework for evaluating agents, from golden datasets to LLM-as-judge and observability tracing