Most of the AI conversation in private equity has been about sourcing, diligence, and value creation. But a quieter transformation is underway inside portfolio teams — closing the gap between when something goes wrong at a portfolio company and when the GP actually finds out.

Stu is joined by Yann Magnan, CEO and Co-Founder of 73 Strings (AI-powered valuation and monitoring platform, backed by Goldman Sachs, Blackstone, Fidelity International, Golub, and Hamilton Lane), and Joe Zein, Co-Founder of Soal Labs (custom AI infrastructure for PE and private credit). One builds the platform. One builds the custom architecture that sits around it. Together they lay out why portfolio monitoring is uniquely hard to automate, and what operators should actually do next.

You'll Learn:

• Why the real bottleneck in portfolio monitoring is infrastructure, not cadence — and why LP demand for weekly/daily reporting is about to force the issue
• What a governed data model actually requires (auditability, versioning, rules, entity resolution) and how it differs from a pile of Excel files
• Why private capital needs 100% accuracy, not 99.5%, and what that means for how LLMs get deployed
• When lighter, cheaper models beat frontier LLMs — and the case for combining ML, NLP and LLMs inside a single ingestion pipeline
• Why "vibecoded" portfolio monitoring tools fail the moment they touch real LP reporting — and how to think about buy vs. build
• The entity-resolution problem (the same company named three different ways across 73 Strings, Salesforce, and your file system) and how to solve it
• What changes in 18 months: daily mark-to-market, AI-surfaced alerts in your inbox, and the prerequisite data foundation that makes it possible

Chapters: 

00:00 – Intro & the 60-day information gap 

02:33 – Why monthly monitoring is brutal: data, process, bandwidth 

06:10 – The real bottleneck at the GP: how reports actually get assembled 

09:20 – Best and worst case timelines for a quarterly close 

10:44 – Is the problem cadence, or infrastructure? 

13:40 – Signals: what shows up before EBITDA moves 

18:57 – New data sources, covenants, and the credit boom 

21:31 – Why "vibecoded" monitoring tools fail 

25:20 – The 100% accuracy problem in private capital 

26:55 – What a robust data model actually looks like 

29:45 – Moving from file systems to governed structured data 

35:49 – Entity resolution across 73 Strings, Salesforce, and the file system 

37:00 – Light vs. frontier LLMs: which do you actually need? 

40:17 – Confidentiality, enterprise plans, and the open-source option 

43:59 – What most people miss about AI and portfolio monitoring 

46:29 – Portfolio monitoring in 18 months 

49:35 – One thing to start doing right now

🎧 Listen on Spotify: https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE 📺 Watch on YouTube: https://www.youtube.com/@JustCuriousAI 🔗 More Expert More interviews: https://checkpluris.com/expert-interviews

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com

Jonathan Hansing is the co-founder of Wallabi, a data, technology, and AI strategy firm helping growth-oriented mid-market companies go AI-native. His team works with CEOs, COOs, and CIOs who feel the pressure to do something with AI — but need a clear path from experimentation to execution. This conversation is full of sharp frameworks and hard-won patterns from the field.

You'll Learn:

• Why "sprinkling AI" is simultaneously a signal of opportunity and a warning sign — and how to tell the difference
• How to assess whether a leadership team actually has the appetite for real AI transformation (not just the desire for it)
• What the J-curve of technology adoption means for AI specifically — and why Jonathan believes companies have about two years to get on the right side of it
• The difference between a company that uses AI and one that is genuinely AI-native — and the single most important shift in mindset that separates them
• A detailed case study: how a media and events company's request for an email automation workflow revealed — layer by layer — a need for full data architecture overhaul, delivered in 8 weeks

Chapters:

00:00 – Intro
00:37 – What Jonathan hopes listeners take away: 10,000 prompts is the new 10,000 hours
02:16 – Who is Jonathan Hansing / what is Wallabi
04:00 – The Wallabi platform: forward-deployed coaches backed by AI
06:30 – A machine learning case study: 18-month problem solved in a week
07:03 – The journey: West Point → Army → Narrative Science → Salesforce → Wallabi
10:08 – "Sprinkling AI": green flag and red flag
12:35 – How Wallabi assesses appetite: the 2-week sprint approach
17:03 – Data philosophy: use case first, architecture second
18:20 – What's happening beneath the surface for pressured CEOs
20:48 – The J-curve: from 20 years to 2 years
25:03 – Dual Transformation: managing two change curves at once
25:49 – AI-native vs. companies that use AI
28:00 – Case study: media & events company — the layered problem
33:09 – What they built on top: Snowflake Intelligence, Tableau, reverse ETL
34:15 – Claude Code and context management: shared learning across repos
36:04 – The ROI of AI is the ROI of WiFi
38:09 – The bifurcation: who becomes unstoppable vs. who plateaus
41:03 – One thing to remember: 10,000 prompts is the new 10,000 hours

🔗 Connect with Jonathan Hansing: https://www.linkedin.com/in/jonathanhansing
🔗 Explore Wallabi: https://www.wallabi.ai
🔗 Explore more interviews: https://checkpluris.com/expert-interviews

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com

Justin Massa and Jason Rubinstein are the co-founders of Remix Partners, an AI strategy consultancy that has now run 32+ kickstarts with small and medium-sized businesses across sectors and geographies. Their perspective is grounded in what actually works — not what looks good in a slide deck — and this conversation is full of practical frameworks for PE investors and operators trying to move AI from an experiment into the business.

You'll Learn:

• Why executive engagement is the single non-negotiable success factor in any AI rollout, and what it actually looks like in practice
• The difference between augment and automate — and why 95% of consulting firms stop at automation when the real leverage is in augmentation
• How to use a three-tier deployment framework (Experiment / Pilot / Implement) to prioritize where AI goes next, and why you need to revisit it every quarter
• What "agent-legible" means — restructuring business information so agents can navigate it efficiently — and a simple mental model for where to start
• A detailed manufacturing case study: how a 70-year-old custom parts company cut its RFQ turnaround time nearly in half using a Claude Code workbench built in 3.5 weeks

Chapters: 

00:00 – Intro
00:33 – Welcome & what Justin and Jason hope listeners take away
01:41 – Who are Justin & Jason / Remix Partners origin
03:19 – Jason's background and why they're doing this together
04:36 – Reflecting on the past year: what changed in the market
07:21 – What's driving the shift in appetite for AI
11:50 – Going wide vs. going deep on AI opportunities
12:55 – Data on AI adoption: where companies actually are
14:42 – Why small businesses are now advantaged over enterprise
15:39 – Operationalizing AI: the Remix kickstart approach
18:21 – Executive engagement as the #1 success factor
19:13 – Show and tell culture: banning secret cyborgs
21:33 – How the approach scales from SMBs to Fortune 25
23:56 – The leader's mandate: making time for AI
25:03 – What went wrong with other consultants
26:48 – The augment vs. automate framework
28:07 – Getting from automation to augmentation
30:33 – The jagged frontier and workforce implications
31:55 – How to navigate capabilities without a static view
33:35 – Deployment zones: Experiment / Pilot / Implement
35:29 – How zones shift quarterly / software stack decisions
37:50 – Manufacturing case study: the setup
40:26 – What they built (workbench in Claude Code, RFQ automation)
42:54 – Who was involved across the organization
44:27 – Impact: clock speed and capacity nearly halved
46:00 – Engineers' time reallocation; divorcing revenue from headcount
47:33 – Agent legibility: what it means and why it matters
49:38 – Making your business agent-legible: the practical how
52:54 – This year we edit documents, we don't create them
53:32 – Where to start for a 50-person company
56:16 – Plug into your core work application and play
58:07 – What happens to companies that don't experiment now
59:11 – Playing to Win in an AI era: differentiation AND low cost
01:02:33 – Closing

• Watch on YouTube: https:/

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com

Kris Krisco, Co-founder of Synopsis, is building AI-native data infrastructure designed specifically for the middle market. After scaling and selling a company under private equity, he saw firsthand why most AI initiatives fail — not because of ambition, but because of broken data foundations. In this episode, he explains how to move from fragmented systems and unreliable dashboards to real-time, operational AI that actually drives EBITDA.

You’ll Learn

Why 97% of middle-market AI projects fail — and the hidden infrastructure gap most companies ignore

The critical role of master data management (MDM) and entity resolution in making AI outputs trustworthy

How AI agents can replace months of traditional data engineering work with one-week iteration cycles

Real examples of workflow automation that reduce operational waste and improve field execution in real time

How private equity-backed companies are using AI in live board meetings to answer strategic questions instantly

Chapters

00:00 Intro
01:00 Why Most AI Initiatives Fail
04:40 The Synopsys Approach to Data Infrastructure
10:40 The MDM Problem No One Talks About
17:15 How Much of Your Data Are You Actually Using?
20:45 Workflow Automation & Real-World Use Cases
25:15 Disaster Recovery Case Study
33:00 AI Inside Private Equity Board Meetings
36:30 The Future: Rule of 40 Becomes Rule of 70+

Links

• Explore more expert interviews: https://checkpluris.com/expert-interviews
• Watch on YouTube: https://youtu.be/a6H8SxJTsOc
• Connect with Kris Krisco: https://www.linkedin.com/in/kris-krisco/

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com

Mollie Amkraut Mueller, Founder of Watch Me AI and former strategist at IDEO and EY, helps non-technical leaders turn AI into a chief of staff, analyst, and revenue engine. In this episode, she breaks down a practical, business-first approach to AI adoption—showing how one recorded meeting can become 10 high-value assets. If you lead a team or operate in the middle market, this is a tactical blueprint for saving time, increasing output, and unlocking ROI without hiring a dev shop.

You’ll Learn

• How to turn a single meeting transcript into LinkedIn posts, proposals, client deliverables, and course content in minutes
• Why most AI projects fail—and how to scope high-ROI use cases that actually stick
• A simple diagnostic framework to uncover 1–10 immediate AI opportunities inside any workflow
• How off-the-shelf tools like Claude and Relay can replace expensive, over-engineered automation projects
• A real-world PR agency case study that cut multi-hour manual research down to 10 minutes per client request

Chapters

00:00 Intro
01:58 Mollie’s background and founding WatchMe AI
05:09 The 90-minute AI diagnostic that reveals hidden ROI
12:17 One transcript → 10 revenue-generating assets
18:55 Why jumping into automations can backfire
20:32 PR agency case study: automating conference research
25:16 Busting myths about AI adoption
29:09 Who should start using AI now

Links

• Watch on YouTube: https://www.youtube.com/watch?v=XEP3Vs5ml4g
• Connect with Mollie Amkraut Mueller: https://www.linkedin.com/in/mollieamkraut/
• Explore more on Just Curious https://www.justcurious.io/articles/ai-for-non-technical-leaders-turn-one-meeting-into-10-revenue-generating-assets

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com

Ryan Kurt, CEO & Founder of The AI Lab, advises middle-market CEOs on how to turn AI from hype into measurable operational advantage. A 13-year veteran of generative AI, he’s helped leaders move beyond “AI strategy decks” to real workflow transformation — including a healthcare network that used computer vision to improve care quality and performance-based pay with 99% accuracy. His perspective is clear: the technology isn’t the hard part — leadership ownership is.

You’ll Learn

• Why most AI initiatives fail because CEOs delegate ownership instead of leading transformation themselves
• How to rebuild workflows from first principles instead of layering AI onto broken processes
• What “total ownership” actually means — and why third-party vendors can’t carry your AI strategy
• How agentic workflows unlock capacity by automating repetitive work before it hits your desk
• A behind-the-scenes case study of using computer vision and human-in-the-loop systems to improve care quality, culture, and compensation

Chapters

00:00 Intro
01:00 Ryan’s path from early AI startup to founding The AI Lab
04:15 Why “AI strategy” is the wrong starting point
08:10 The leadership readiness test most companies fail
12:00 What total ownership actually looks like
18:00 Why agentic workflows change the economics of work
23:00 Case study: Using AI to improve caregiver performance
27:30 Human-in-the-loop systems and hybrid AI architecture
30:00 Cultural and operational impact
31:45 How to sequence AI adoption the right way
34:25 Who The AI Lab works with

Links

• Watch on YouTube: https://youtu.be/bdSq4S2_PZU
• Connect with Ryan Kurt: https://www.linkedin.com/in/ryankurt/
• Explore more interviews: https://www.justcurious.io/articles/how-ai-caught-what-bonuses-training-and-pay-raises-couldnt-fix"

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com

Adam Schwartz, Co-founder and CEO of Parable, explains why most enterprise AI initiatives fail before they ever touch the P&L. Drawing on firsthand work with Fortune 500 and private equity–backed companies, he breaks down how hidden operational inefficiencies block real transformation—and what leaders must measure to unlock durable AI-driven value.

You’ll Learn

• Why executives consistently misjudge how teams actually spend their time—and how that blind spot derails AI strategy
• How “keep-the-lights-on” work and operational debt quietly consume 50–70% of enterprise capacity
• Why data warehouses and dashboards lack the context required for effective AI agents and decision-making
• How mapping work at the activity level enables AI use cases to be prioritized by ROI, not intuition
• What Sunrun uncovered when it quantified technical and operational debt—and how that led to $80M+ in savings

Chapters

00:00 Intro
01:33 Why leaders lack visibility into real work
05:12 The hidden cost of “keep the lights on” work
09:10 Why data warehouses fail at operational understanding
13:20 What a contextual work graph actually looks like
18:29 Measuring AI impact beyond adoption
23:46 Operational debt and resource allocation
30:01 Sunrun case study: uncovering $80M in inefficiencies
35:43 AI vs. process change—what to automate and why
41:57 The mindset shift leaders need for AI at scale

Links

• Watch on YouTube: https://youtu.be/zDDspYqT_7o
• Connect with Adam Schwartz: https://www.linkedin.com/in/adamgregoryschwartz/
• Explore more interviews: https://www.justcurious.io/articles/how-sunrun-found-80m-in-hidden-costs-by-finally-understanding-its-data

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com

Arman Hezarkhani is the Co-founder & Managing Partner of Tenex, an AI transformation firm helping middle-market and growth companies turn AI from hype into real P&L impact. Drawing from experience scaling Google’s cloud AI tools and building venture-backed startups, Arman breaks down how leaders can actually operationalize AI — not just talk about it. This episode is a practical guide for investors and operators who want measurable results, fast.

You’ll Learn

Why AI adoption is easy for individuals but structurally hard for organizations — and how to close that gap

How to identify high-ROI AI use cases by starting with business goals, not technology

What “AI slop” looks like in practice and how to avoid wasting capital on vaporware

Why Tenex charges on output (story points) instead of hours — and how incentives unlock 10x productivity

How to get your first AI win inside a company to build momentum and executive buy-in

Chapters

00:00 Intro
00:49 Arman’s background and founding Tenex
02:30 Why AI is different from past tech waves
05:26 What AI changes — and what it doesn’t — in business
07:08 How companies should prepare for an AI-native future
09:01 How Tenex identifies and executes AI opportunities
12:15 Diagnostic process and opportunity mapping
15:13 Why CEO-level buy-in is critical
16:31 The Tenex output-based business model
21:28 Generative AI, agents, and AI-powered software builds
23:25 Avoiding “AI slop” and navigating the AI bubble
27:00 Build vs. buy decisions in an AI world
28:39 Real client example: AI-driven revenue and efficiency gains
33:24 Ruthless focus on business goals
34:02 Advice for new Heads of AI

Links

• Watch on YouTube: https://youtu.be/xRV_OnkK9eA
• Explore more interviews: https://www.justcurious.io/
• Connect with Arman Hezarkhani: https://www.linkedin.com/in/ahez/

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com

Evan Glaser, Founder & CEO of Alongside AI, joins Just Curious to break down how middle-market companies can actually deploy AI, without blowing up risk, compliance, or trust. Drawing on a decade of Fortune 100 experience, Evan explains why governance isn’t a blocker to AI value creation, but the accelerator most operators are missing.

You’ll Learn

Why AI governance is the fastest way to unlock real productivity—not slow it down

How to design practical AI guardrails that let teams innovate safely and move faster

Where middle-market companies typically underestimate their AI risk exposure

How to turn siloed institutional knowledge into a scalable, AI-powered asset

A real-world case study showing how AI reduced onboarding time and unlocked new revenue

Chapters

00:00 Intro
01:24 Why middle-market leaders struggle to start with AI
02:59 Governance as an accelerator, not a brake
04:41 Defining AI guardrails that actually work
06:47 Common governance gaps and blind spots
09:03 What “good” AI governance looks like in practice
14:02 Unlocking trapped institutional knowledge with AI
19:59 Case study: AI-powered onboarding and faster ramp times
30:23 Turning AI questions into a new advisory revenue stream
33:54 The risks of shadow AI
36:13 Lessons from regulated industries that apply everywhere

Links

• Watch on YouTube: https://youtu.be/OXYdxzPgPKg
• Connect with Evan Glaser: https://www.linkedin.com/in/glaserevan/
• Explore more interviews: https://www.justcurious.io/experts/evan-glaser

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com

Alex Gruebele and Chandler Gonzalez, co-founders of Tau9 Labs, build custom AI systems that solve the messy, high-value problems generic manufacturing software can’t touch. With roots in robotics, hyperscale software, and logistics, they focus on the workflows where context matters most—quoting, downtime, document analysis, and technician knowledge. Their work shows how applied AI can transform mid-market industrial operations in months, not years, by fitting the way factories actually run.

You’ll Learn

• Why generic SaaS breaks in manufacturing—and how custom AI can match real-world workflows without forcing process changes.
• How decades of unstructured documents hide the root causes of downtime, warranty costs, and defects—and how AI can surface them.
• A practical approach to turning vague “AI requests” into solvable, ROI-positive operational problems using on-site discovery and the five whys.
• How AI-accelerated engineering compresses build cycles from quarters to weeks, enabling tools like instant quoting platforms.
• When to build vs. buy: the signals that indicate a custom solution will outperform configurable off-the-shelf tools.

Chapters

00:00 Intro
01:00 Backgrounds & the origin of Tau9 Labs
03:20 Why manufacturing lags in software adoption
05:10 Common operational problems & unstructured data
07:15 Downtime, tribal knowledge, and automation
08:50 Quoting workflows and workflow automation
09:50 How Tau9 diagnoses the “real” problem
12:20 On-site discovery and the five whys
15:20 Case study: injection molding quoting platform
18:10 Designing for speed and user behavior
20:15 Building the platform in four months
22:50 Results: conversion lift & revenue impact
25:10 Build vs. buy in industrial AI
28:45 Why on-site U.S. engineers outperform remote teams
31:20 How AI changes software design principles
33:50 Who Tau9 Labs is best suited to help
35:50 Closing

Links

• Explore more interviews: https://www.justcurious.io/articles/how-tau9-labs-helped-a-manufacturer-cut-quoting-time-from-hours-to-seconds----and-double-win-rates
• Watch on YouTube: https://youtu.be/njuUtQucqHU
• Connect with Alex Gruebele: https://www.linkedin.com/in/alexgruebele/
• Connect with Chandler Gonzalez: https://www.linkedin.com/in/chandlergonzales/

Just Curious is the content and podcast brand of Pluris, helping leading operators and investors cut through a noisy AI market and find the right partners — from strategy and assessments to full-stack AI development. Our network of applied AI experts spans generative and agentic AI, workflow automation, and custom AI-powered software, helping teams turn curiosity into measurable value.

Explore more interviews and connect with experts at https://www.checkpluris.com

 Subscribe to our newsletter at https://just-curious-ai.beehiiv.com