Stu Willson interviews Csongor Barabasi, CEO of Bonsai Labs, on how PE-backed B2B software companies can go from AI idea to production fast—without massive data transformations. Barabasi breaks down a people-process-product approach, weekly sprint cadence, and evaluation-first mindset that turned a legal AI assistant from concept to GA in six months with 7-figure ARR, tying outcomes to EBITDA and net-new revenue.

Chapters:

00:00 – Intro & why this episode matters for PE-backed SaaS
01:30 – Who is Csongor Barabasi? What is Bonsai Labs? Execution over slide decks
03:45 – Founder journey: from ML engineer to AI builder focused on distribution
06:15 – Two adoption traps: inaction & “we must finish data overhaul first”
08:04 – How to start: 4–6 week POCs tied to EBITDA, margin, net-new ARR
09:13 – What CTOs worry about: testing stochastic systems & POC→prod gap
11:18 – The people-process-product (PPP) model for real AI transformation
13:53 – Speed & method: domain deep-dive, eval datasets, weekly sprints
16:40 – Time to prod: examples shipping in 4–12 weeks
17:47 – Case study 1 (Legal AI): problem, Azure setup, eval focus, doc scale
20:24 – Results: beta in 3 months, GA in 6, 7-figure ARR, fundraising unlocked
21:16 – How incumbents beat AI-native entrants: iterate with customers fast
23:49 – Case study 2 (PE-backed legacy SaaS): new AI features → higher ACV/stickiness
26:21 – Differentiating from “AI” dev shops: real NLP pedigree, ex-founders, elite talent
30:53 – Misconception to drop: “no AI until data is perfect”—prove ROI first
32:28 – Building AI teams: on-site interviews, single KPI to track experiments
34:43 – What to stop: pet POCs—prioritize the highest-ROI pilot now
36:02 – Who should reach out to Bonsai Labs (CTOs/CPOs in B2B SaaS)
36:45 – Close

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