https://www.gtmaipodcast.comhttps://www.fluint.comIn this episode of the GTM AI Podcast, I sit down with Nate and John, co-founders of Fluint, for a kitchen-deep look at how they built Ollie, an omni-agent that captures tacit sales knowledge and surfaces it across an entire enterprise sales org. We cover:The tacit knowledge problemWhy a small pocket of reps wins 50-60% of pipeline while everyone else sits at 17-19%, why "clone your top rep" has been said for 15 years and never delivered, and what happens to your $30M deal architect when they take a new job two weeks from now.The implicit signal exampleA real story of two reps, two POC readouts, two procurement follow-ups (one at 9pm Friday, one Wednesday during business hours), and why top reps will negotiate completely differently on the same data while average reps miss the signal entirely.The photo vs. video architectureWhy most AI tools treat data as a snapshot (LLM context = one photo), and why Fluint's event-driven, time-series architecture treats it as a video. The 10-Second Tom analogy from Fifty First Dates that explains why LLMs alone cannot solve this problem.The ML + LLM stackJohn walks through the architectural decision: ML for pattern recognition and judgment layer, LLM for human-to-human interaction. "Using the right tool for the right job." This is the most underrated decision in enterprise AI right now.Ollie's omni-agent designWhy one AI teammate beats 130 task-specific agents. The 75% of users who gender their AI. The trust dynamics that make a sales rep follow an agent's advice when it runs counter to the playbook.The racehorse modelHow Fluint runs a global baseline model and a customer-specific model in parallel, evaluates them nightly, and promotes the winner. Continuous evaluation as the moat.The "data is a product of people" answerJohn on why perfect data is a logical fallacy and what to do instead. The single line that changes how you approach AI readiness.Real outcomes+$28K added to ACV per team per year. 32 days off the median sales cycle. The maturity curve from Q1 (win existing deals with less discount) through year-end (win deals you would have lost).GUESTSNate, Co-founder & CEO, Fluint (the "second brain")Repeat enterprise sales leader and repeat founder. Built Fluint from a problem he could not solve as a sales leader. Author of two books on tacit knowledge and executive sound-bite communication.John, Co-founder & CTO, Fluint (the "first brain")Technical co-founder. Builds the systems that turn Nate's crazy ideas into shipping product. Specialty: event-driven architectures and ML-as-judgment-layer for enterprise sales.LinkedIn Nate:https://www.linkedin.com/in/natenasralla/Linkedin John:https://www.linkedin.com/in/jon-crawley-3797a8100/Blog: fluint.io/blogJohn's recent technical guide: building enterprise AI agents (just published on the Fluint blog)GitHub repo (DIY resources for time-series-data agent architecture): linked from blog