ThinkData Podcast

Agentic engineering is quickly becoming one of the most important shifts in software development.
In this episode, I sit down with Scott Breitenother, Co-Founder & CEO of Kilo Code, to discuss why individual coding assistants won't drive the future of software development, but by autonomous AI agents capable of planning, building, testing, and shipping software.
Scott shares the journey from building and selling Brooklyn Data Company to launching Kilo, an open-source agentic engineering platform designed to help developers become dramatically more productive in the AI era.
We discussed product-market fit, engineering adoption, the realities of competing with Cursor, Copilot, and Claude Code, and what engineering leaders should be thinking about as AI fundamentally changes how software teams operate.
If you're a founder, engineering leader, developer, or simply interested in the future of AI-powered software development, this is an episode you won't want to miss.

In this episode of the ThinkData Podcast, I welcome back Dimer Health Co-Founders Carrie Hodge, Sarig Reichert, alongside Chief Medical Officer Dr. David Feldman.
Fresh off the announcement of their $13.5 million Series A, the team discusses why the 30 days following hospital discharge remain one of the most broken parts of healthcare, and how Dimer is using clinician-led AI to transform patient outcomes.
The conversation covers scaling after fundraising, building culture during hypergrowth, the realities of operating at Series A, and what it takes to create a new layer of healthcare infrastructure.

Today, we’re getting into where AI actually fits in the finance stack and why most teams are getting it wrong.
I’m joined by Deepak Bapat, CTO and co-founder of Tabs, an AI-native platform automating contract-to-cash end-to-end.
We cover:
The difference between AI-native vs AI-layered products
Why finance has near-zero tolerance for AI errors
The technical challenge of automating contract-to-cash workflows
Integrating AI into fragmented ERP ecosystems
What finance teams may look like in the next 3–5 years as AI agents take over operational workflows

A really interesting conversation around where AI creates real operational leverage, and where the hype still outweighs the reality.

Most enterprise AI isn’t failing because of the models…
It’s failing because no one trusts the output.
In this episode of the ThinkData Podcast, we’re joined by Uri Knorovich, CEO of Nimble, to unpack the growing trust gap in enterprise AI and why bad, delayed, and fragmented data is becoming the biggest blocker to real AI adoption.
We explore:
• Why AI works in demos but breaks inside enterprises
• The hidden risk of AI outputs without traceable data lineage
• Why trusted, live data infrastructure matters more than model speed
• The shift from “is the AI fast?” to “can we trust the decisions?”
• Real-world examples where AI and live data are already driving value
A great conversation on the future of enterprise AI, data infrastructure, and building systems companies can actually rely on.

On today’s episode, we’re joined by Sadegh Salehi, Director of Research and Principal Scientist at Overjet, a Series C AI-powered dental platform spun out of MIT.
Overjet is using computer vision and AI to analyse dental X-rays, helping dentists, insurers, and patients make more accurate, transparent, and data-driven care decisions.
In this episode, we explore:
• The hardest machine learning challenges in dental imaging
• Building AI systems clinicians and insurers can trust
• Why Overjet expanded from software into hardware
• What it really takes to drive AI adoption inside clinical workflows
• The future of AI in dentistry over the next 5–10 years
A fascinating conversation on applied AI, healthcare infrastructure, trust, and the future of medical imaging.