How do you compete with billion-dollar marketing tech giants when you're customer-funded? Today's guest has the answer – and it involves rethinking everything about how B2B buyers consume content.

I'm joined by Geoff Rego, CEO and Co-Founder of Hushly, an all-in-one personalization platform that's helping companies like NVIDIA transform their digital experiences. After Oracle acquired his previous marketing automation company, Geoff came back to solve a bigger problem: making B2B content experiences actually work for buyers, not just marketers.

Hushly takes a radically different approach to B2B marketing. Instead of leading with forms and gating content, they've built AI-powered experiences that let buyers self-educate at their own pace. Think Netflix for B2B content – where AI agents do the searching, finding, and personalizing so buyers can focus on learning.

In this episode, Geoff breaks down how they're using AI to create account-specific microsites at scale, why "content Sherpas" beat traditional chatbots, the power of first-party intent data, and how being customer-funded forced them to build better products than their VC-backed competitors.

Takeaways

• The Netflix Model for B2B: Why self-nurturing beats email nurturing - let buyers binge content when they want it
• AI Microsite Generation: How to automatically create personalized sites for 50 accounts using internal + public intelligence
• First-Party Intent Revolution: Stop inferring what buyers want - let them tell you through conversational AI
• The Form-Free Future: Leading with value increases content engagement by 300% and qualified leads by 51%
• Customer-Funded Advantage: How resource constraints force better product decisions than unlimited VC money
• Content Memory: Why AI that remembers past interactions creates exponentially better buyer experiences

Sound Bites

• "We're not big proponents of leading with a form, we're big proponents of leading with value, kind of like the Netflix binge experience."
• "Don't call it bootstrap. Call it customer-funded."
• "When you have too much money, common sense goes out the door."

Chapters  
01:13 - What is Hushly? 
02:08 - Customer Examples and Scale 
03:42 - Core Platform Capabilities 
06:20 - Account Intelligence and Data Sourcing 
09:31 - AI-Generated Microsites 
11:53 - Dogfooding Their Own Platform 
13:07 - AI Adoption Friction in B2B 
14:39 - Content Hub Architecture 
16:50 - First-Party Intent Through Conversational AI 
19:34 - Personalized Generative AI in Real-Time 
20:57 - Self-Nurturing Landing Pages 
25:11 - The YouTube Model for B2B 
27:50 - Future Product Direction 
30:06 - Competing with Billion-Dollar Brands 
32:20 - Advantages of Being Lean 
34:07 - Building a 20-Year Team 
35:51 - Advice for Customer-Funded Startups 

Connect with us
Where to find Geoff:
LinkedIn: https://www.linkedin.com/in/geoffrego/
Website: https://hushly.com/

Where to find Sani:
LinkedIn: https://linkedin.com/in/sani-djaya/
Get in touch: [email protected]

Most employee surveys deliver generic insights that executives already know. But what if AI could predict organizational risks months before they impact your bottom line?

Today I'm joined by Kara Whitaker, VP of Client Partnerships at Etnromy. Entromy helps private equity firms and their portfolio companies get fast, unfiltered reads on organizational health. They're not just running surveys - they're building AI-native intelligence that surfaces blind spots, alignment gaps, and risks that traditional consulting approaches often miss.

What makes Entromy different is their approach to AI. Instead of bolt-on features, their models analyze feedback in real time, learning from 700+ PE-backed organizations to deliver predictions and recommendations tailored to each company's specific context. While McKinsey and BCG might take weeks to uncover organizational issues, Entromy delivers actionable insights in days.

In this conversation, Kara breaks down how they're applying AI across the entire client lifecycle - from data collection through predictive analytics that flag risks six months in advance. We dive into their upcoming PE Dashboard, how they're building AI agents for automated reporting, and why their lean team is punching above its weight with smart AI tooling.

Takeaways

• Cross-Portfolio Learning: Entromy's AI learns from 700+ PE-backed organizations simultaneously, delivering benchmarked insights instead of isolated analysis
• Predictive Risk Flagging: Moving beyond reactive surveys to AI that identifies organizational risks 6 months before they impact EBITDA
• Tailored Recommendations by Function: AI generates specific action plans filtered by department, manager, and team - not just company-wide generic advice
• PE Dashboard Intelligence: New dashboard gives PE firms portfolio-wide visibility to identify which companies need immediate attention
• AI-Powered Consultant Acceleration: What takes McKinsey weeks, Entromy delivers in days, enabling consultants to deliver faster at lower cost
• Smart Tool Stack for Lean Teams: Using Momentum, Gamma AI, and Intercom Copilot to automate low-leverage tasks and focus on strategic client work

Sound Bites

"Most companies have the same problems. There's three key categories that most organizations score the lowest in: performance management, capabilities, and communication."

"What McKinsey and BCG might uncover in weeks, Entromy delivers in days through AI native platforms."

"Our AI is not just learning from your survey - it's learning from every other organization that's running in our platform simultaneously."

"If you're two weeks behind in private equity, you might as well be a year behind."

"AI is not replacing the human touch. It really and truly is amplifying it and helping a small but mighty CS team punch above its weight."

"I genuinely think everybody needs Entromy. I don't care if you've got all the answers - there's always opportunities for improvement."

Chapters

00:00 - Introduction to Entromy and Organizational Intelligence 
01:21 - The Problem: Getting Truth About What's Happening Inside Companies
 02:44 - How AI Powers Every Part of the Workflow 
04:35 - AI in Data Collection: Beyond Generic Survey Results 
06:29 - Cross-Portfolio Learning and Benchmarking 
08:58 - Tailored Recommendations by Function and Department 
14:09 - Three Customer Personas: PE Firms, Portfolio Companies, Consultants 
16:07 - Upcoming PE Dashboard for Portfolio-Wide Risk Assessment 
20:09 - AI Tools That Supercharge Lean CS Teams 
28:55 - Favorite AI Tool: Momentum for Salesforce Integration 
33:35 - Vision for 2025: Predictive Analytics and AI Agents 
35:49 - What Kara is Most Proud Of 

Connect with us

Where to find Kara
LinkedIn: https://linkedin.com/in/kara-whitaker/
Website: https://entromy.com/

Where to find Sani
LinkedIn: https://linkedin.com/in/sani-djaya/
Get in touch: [email protected]

Tags

#AI #MachineLearning #PrivateEquity #OrganizationalHealth #CustomerSuccess #PredictiveAnalytics #AIAgents #EmployeeSurveys #PortfolioManagement #AITools #Momentum #GammaAI #IntercomCopilot #BusinessIntelligence #PEPortfolio #ValueCreation

Mario reveals the fascinating story behind Vengreso's transformation, including the challenges of transitioning from a service-based model to software, the discovery that led to FlyMessage's creation, and how they've built an integrated suite of AI tools that save sales professionals an average of 30 hours per month.

Takeaways

• Service-to-SaaS Pivot Strategy: Mario explains the 13-month transition process from a training company to a SaaS business, including the investor feedback that forced the decision and the challenges of building without a technical co-founder.
• AI Training with Domain Expertise: Vengreso leverages years of sales training content and proven methodologies to prompt engineer their AI tools, creating more effective sales-specific responses than generic AI solutions.
• Workflow Integration Philosophy: The key to building a sustainable competitive advantage isn't just individual features, but creating an "X-in-one" product strategy that embeds deeply into users' daily workflows.
• Product-Led Growth Challenges: Mario candidly discusses the difficulties of transitioning from enterprise sales expertise to mastering product-led growth, and the importance of driving daily usage for PLG success.
• Customer Discovery Through Data: The breakthrough moment came when Mario discovered that despite 97% customer satisfaction with their training, only 15% of people were actually implementing the strategies due to time constraints in content creation.

Sound Bites

• "It took us 25 months to be able to type the first 1.1 billion characters and only 10 months to type the next 1.1 billion characters."
• "I as a sales leader, top sales influencer, a leader over the largest sales prospecting training company globally, I can't beat the AI over 80%."
• "A fool with a tool is still a fool."
• "If you don't love something, if it hurts, it only impacts you. It doesn't take away from your family - always take it away from your sleep."

Chapters

00:00 - Introduction Sani introduces Mario Martinez Jr. and sets the stage for discussing Vengreso's transformation from service to SaaS.

03:12 - The Vengreso's Origin Story Mario shares the backstory of Vengreso's creation through a seven-way company merger and their growth into the world's largest sales prospecting training company.

07:38 - The Service to SaaS Transition Deep dive into the pivotal decision to transition from training services to software, including investor feedback and market challenges during COVID.

13:17 - Building Without a Technical Co-founder Mario discusses the challenges of going through two technology teams and the lessons learned from starting without technical leadership.

15:10 - The Customer Satisfaction Paradox The discovery that high customer satisfaction didn't correlate with implementation, leading to the insight that drove product development.

19:10 - Explosive Growth in Usage Mario reveals the dramatic growth in character usage that validated their product-market fit with AI-powered features.

23:13 - Competitive Advantage Through Integration Discussion of how Vengreso builds sustainable competitive advantages through workflow integration rather than individual features.

27:27 - The Product Suite Expansion Overview of FlyEngage, FlyPost, FlyGrammar, and FlyRoleplay - the growing ecosystem of AI-powered sales tools.

33:08 - Mastering Product-Led Growth Challenges and strategies for transitioning from enterprise sales to product-led growth, including usage-based feature throttling.

35:29 - Future Vision and Roadmap Mario outlines the next 12 months and five-year vision for Vengreso, including personality-based messaging and CRM integration.

39:32 - Funding and Scale Challenges Discussion of resource constraints and how additional funding would accelerate product development and marketing.

41:28 - Personal Philosophy on Work-Life Balance Mario shares his approach to entrepreneurship while maintaining strong family relationships.

44:07 - Wrap-up and Contact Information How to connect with Mario and try FlyMessage, plus final thoughts on the conversation.

Connect with us
Where to find Mario
Website: https://vengreso.com/
LinkedIn: https://www.linkedin.com/in/mthreejr/
Company LinkedIn: https://www.linkedin.com/company/vengreso/

Where to find Sani: 
LinkedIn: https://www.linkedin.com/in/sani-djaya/
Get in touch: [email protected]

#AIsalestools #SaaStransformation #Salesproductivity #B2Bsalestechnology #SaaSPivot

Explore how artificial intelligence is transforming the traditionally manual world of mergers and acquisitions financial analysis.

Derek shares how Socratic AI is solving a massive pain point for investment bankers and M&A advisors who spend countless hours cleaning up messy financial data from private companies. From Excel spreadsheets to PDF bank statements, Derek explains how his team uses a sophisticated combination of LLMs, pattern matching, and custom algorithms to normalize chaotic financial documents into professional-grade models.

This conversation dives deep into the technical challenges of parsing tabular financial data, the strategic decisions around when to use different AI models, and how the latest reasoning models are being applied to spot financial anomalies that could impact multi-million dollar deals.

Takeaways

• The M&A Data Problem: Private company financials are often messy and unstructured, requiring hours of manual cleanup before analysis can begin
• Smart Model Selection: Success comes from using the right AI model for each specific task - not just throwing everything at the most powerful LLM
• OCR vs. LLM Trade-offs: Even with advanced models, extracting tabular data from PDFs remains challenging and requires hybrid approaches
• Reasoning Models in Action: New reasoning capabilities are being used to hunt for financial anomalies and errors that could cost millions
• The Ferrero Rocher Effect: Foundation models are just the "peanut in the center" - the real value comes from all the layers around it (workflow orchestration, domain expertise, user experience) that create the full delicious experience
• The Vertical SaaS Advantage: The real value isn't in the AI models themselves, but in orchestrating multiple models into domain-specific workflows
• Productivity Multiplier: Small AI-native teams can now accomplish what would have required 10x more people just a few years ago

Sound Bites

• "We use a combination of pattern matching, rules, and large language models to interpret and standardize financial data - you can't just throw it into ChatGPT and get an output."
• "If a column gets off by one in financial data, you've screwed up the entire thing - the integrity of that table needs to be maintained."
• "I feel like I'm ten people now and I'm doing the job of what would have been 10 people."
• "Investment banking analysts work 80-100 hour weeks because they're going cell by cell, formula by formula - we can set an AI that doesn't get tired to do that type of deep thinking."
• "The foundation model is just the peanut in the center - everything around it is the deliciousness that adds to the whole Ferrero Rocher."

Chapters

00:00 Introduction and Socratic's AI Overview 
01:46 The M&A Analyst Workflow Problem 
04:18 Types of Financial Documents and Data Sources 
05:21 AI Techniques for Data Normalization 
07:02 Choosing Between LLMs and Algorithms 
08:32 PDF Processing and OCR Challenges 
11:55 Post-Normalization Analysis and Features 
14:45 Rule-Based vs AI-Driven Analysis 
17:16 Reasoning Models and Parallel Processing 
21:20 Visual Reasoning Capabilities 
23:45 The "Wrapper" Debate and Value Creation 
26:39 AI Tools the Team Uses Daily 
29:47 Prototyping Tools and Workflow Evolution 
34:29 Future Roadmap for Socratic's AI 
37:14 Personal Values and Work-Life Balance 
38:43 How to Connect and Get Involved

Connect with us
Where to find Derek
Website: https://socratics.ai
LinkedIn: https://www.linkedin.com/in/bomanderek/

Where to find Sani: 
LinkedIn: https://www.linkedin.com/in/sani-djaya/
Get in touch: [email protected]

#AI #MachineLearning #MergersAndAcquisitions #FinTech #StartupTech #LLM #ReasoningModels #VerticalSaaS #FinancialAnalysis #InvestmentBanking

Ever wonder how companies like DHL Express manage to deliver thousands of packages efficiently every single day? The secret lies in sophisticated AI-powered routing and dispatch automation - and today's guest is at the forefront of this revolution.

Join me as I sit down with Erin Blair, VP of Global Partnerships at Wise Systems, an MIT-born company that's transforming how businesses handle their last-mile delivery operations. WISE Systems processes 20,000-30,000 routes daily using machine learning to capture the "tribal knowledge" of experienced dispatchers and drivers, turning it into automated systems that optimize operations in real-time.

In this fascinating deep-dive, Erin reveals how AI is solving one of logistics' biggest challenges: turning reactive dispatch operations into proactive, data-driven systems that can identify when trucks are running 30% empty and immediately alert sales teams to fill that capacity.

Key Takeaways

• AI-Powered Route Optimization: How machine learning captures dispatcher preferences, driver patterns, and service times to optimize 20,000+ routes daily
• Turning Operations into Sales Opportunities: Real-time visibility into truck capacity creates immediate revenue opportunities for sales teams
• The Power of Field Research: Why WISE Systems sends engineers and founders on actual delivery routes across different countries to understand real user needs
• Proactive vs Reactive Operations: How AI is transforming dispatchers from firefighters into strategic planners with data-driven insights
• Tribal Knowledge Capture: How machine learning preserves and scales the expertise of veteran dispatchers and drivers
• Real-Time Business Intelligence: Moving from quarterly reports to instant feedback on route performance and capacity utilization

Sound Bites

• "I talked to a national carrier recently - they're running 30% full on half their trucks. How does the salesperson know that?"
• "We want to automate and give that dispatcher more time to deal with the exceptions, not the rules"
• "One of our founders will sit in a truck with somebody and deliver with them. That's impressive."
• "You plan to do 1800 miles today with 12 trucks - how did you actually do? I'll find out in a couple months when my quarterly report comes in. Well, that doesn't help them for next Tuesday."
• "If you're not in the game, you can't lose it - but you can't win it either"

Chapters
00:00 - Introduction to WISE Systems and Last-Mile Logistics
02:16 - What WISE Systems Does: Routing & Dispatch Automation
04:15 - Scale and Major Customers (DHL Express, Anheuser-Busch)
05:34 - AI and Machine Learning in Route Optimization
09:32 - Turning Empty Truck Capacity into Sales Opportunities
12:18 - Being Proactive vs Reactive in Operations
15:51 - AI Tools and Technology Stack at WISE Systems
18:57 - Internal AI Tools for Support and Engineering
24:22 - Field Research: Engineers on Delivery Routes
29:41 - The Future: Changing the Role of Dispatchers
33:40 - Personal Values and Family as Greatest Achievement
38:59 - How to Connect with Erin and WISE Systems

Connect with us
Where to find Erin:
LinkedIn: https://www.linkedin.com/in/erin-blair/
Website: https://www.wisesystems.com/

Where to find Sani: 
LinkedIn: https://www.linkedin.com/in/sani-djaya/
Get in touch: [email protected]

Ever wonder how massive sales forces at companies like Microsoft, Salesforce, or Databricks manage to consistently hit their targets and understand complex customer needs? A huge part of the answer lies in sophisticated, AI-driven insights, and today's guest is right at the heart of building that technology.

Today, I'm joined by Frank Wittkampf, Head of Applied AI at DataBook. DataBook is a platform designed to supercharge enterprise sales productivity. They don't just offer generic AI; they build deeply specialized systems that analyze vast amounts of data – financial reports, news, competitive landscapes, even proprietary insights – to tell salespeople exactly what to position, why, and when.   

They are moving beyond simple chatbots or free-form AI agents. DataBook focuses on applied AI, using what Frank calls 'guided reasoning' to ensure the insights delivered are consistent, reliable, and directly drive sales outcomes, like significantly increasing deal sizes.   

In this episode, Frank dives into how DataBook's AI works, why a 'guided' approach beats pure agentic systems in enterprise, the surprising challenge of people over-imagining AI's current capabilities, how they navigate the R&D frenzy to deliver real value, and their vision for a future where AI proactively coaches you.

Takeaways

• Why "Guided Reasoning" Beats Pure AI Agents: Enterprise needs predictable, repeatable outcomes - not creative exploration
• The Over-Imagination Problem: Why Computer Use and other flashy AI features aren't ready for enterprise deployment
• Data Strategy That Works: How Databook combines public data, proprietary datasets, and pre-solved analysis for instant insights
• R&D vs Reality Balance: Practical framework for experimenting with cutting-edge AI while delivering customer value
• The Future is Proactive: Why the next leap in AI isn't just responding to queries, but actively coaching users
• Enterprise Integration Challenges: Real talk about data access, security approvals, and building trust with large customers

Sound Bites

• "Free reasoning is all fun and great, but in enterprise, fully free reasoning is just not that helpful."
• "Computer use is incredibly useful... The problem with it is it's just so non-practical at the moment. It is incredibly slow."
• "For AI to deliver you a proper answer, you actually need to pre-solve that answer pretty thoroughly if you want to do a good job at it."
• "We can see deal sizes increase by 1.9 to 2x when people are actively using this."
• "The big change that's coming in AI is not just you engaging with it, but it engaging with you and helping you."

Chapters
00:00 - Introduction to Databook and Enterprise AI Reality
03:08 - What is Databook? Serving Microsoft, Salesforce & Databricks
04:33 - AI-Native Features: Beyond Simple LLM Implementations
06:17 - Customer Deep Dive: Why Big Tech Companies Choose Databook
09:18 - Proprietary Data Strategy and Pre-Solved Analysis
11:03 - Day-to-Day as Head of Applied AI: Product to Engineering Translation
14:21 - Balancing R&D Innovation with Customer Results
18:58 - Testing and Experimentation in Enterprise AI
21:14 - Dogfooding: How Databook Uses Its Own Product Internally
23:24 - What's Next: The Push Toward 4x Deal Size Increases
25:12 - Guided Reasoning: The Middle Ground Between Workflows and Agents
26:19 - Biggest Roadblocks: Enterprise Speed and Data Integration
27:49 - Technical Deep Dive: Delta Lake and Joint Data Access
30:07 - What Frank is Most Proud Of

Connect with us
Where to find Anthony:
LinkedIn: https://www.linkedin.com/in/wittkampf/
Medium: https://medium.com/@frankw_usa
Website: https://databook.com/

Where to find Sani:
LinkedIn: https://linkedin.com/in/sani-djaya/
Get in touch: [email protected]

My first ever guest on the podcast is Anthony Bay — a veteran product and technology executive with decades of experience shaping some of the world’s most impactful tech platforms.

After starting his career in early startups, he spent eight years at Apple across the U.S. and Europe leading product marketing efforts in networking, communications, and media. He then moved to Microsoft, where he launched the original MSN, and led major product groups focused on e-commerce and digital media through the 1990s.

Anthony later took on a global leadership role at Amazon Prime Video in its earliest phase and went on to become CEO of Rdio, a digital music streaming company acquired by Pandora.

Today, he’s CEO and founding partner at Techquity, an advisory firm made up of senior product and engineering leaders from companies like Amazon, Google, and Microsoft. Techquity helps CEOs and investors navigate complex tech and AI decisions by embedding experienced operators directly into the process — from hiring and team-building to product strategy and infrastructure modernization.

In this episode, Anthony and I talk about what AI means for modern execs, how non-technical leaders can make smart bets, and how seasoned operators are guiding the next wave of transformation.

Takeaways

• AI is transforming how businesses operate and make decisions.
• Techquity helps non-technical leaders navigate complex tech challenges.
• Data governance is crucial for leveraging AI effectively.
• Companies must focus on building a tech culture to innovate.
• The pace of AI development is unprecedented and offers new opportunities.
• Understanding data sources is key to creating value with AI.
• Experimentation with AI tools is essential for staying competitive.
• Organizations need to prioritize what to build before how to build it.
• Family and personal values are important for work-life balance.
• Techquity aims to raise awareness of its unique advisory services.

Sound Bites

• "AI is transforming how businesses operate."
• "Experimentation with AI tools is essential."
• "The pace of AI development is unprecedented."

Chapters

00:00 - Introduction to AI and Tech Leadership

06:20 - Anthony Bay's Career Journey

10:19 - Techquity: Bridging the Tech Gap

13:05 - Navigating AI in Business

14:41 - Enhancing Business with AI

20:59 - Data Governance and Quality

26:48 - Assessing AI Tools in Organizations

32:56 - Understanding Organeering

36:42 - Vision for Techquity in 2025

39:39 - Personal Reflections and Legacy

Connect with us
Where to find Anthony:
LinkedIn: https://linkedin.com/in/anthonybay/
Website: https://techquity.ai/

Where to find Sani:
LinkedIn: https://linkedin.com/in/sani-djaya/
Get in touch: [email protected]