The Tech Trek

In this episode, Amir hosts Healey Cypher, CEO at BoomPop and COO at Atomic. The discussion covers various aspects of being a good and successful founder, what makes an idea backable, and the differences between successful and unsuccessful entrepreneurs. Healey shares his background as a serial entrepreneur and gives insights into his companies, BoomPop and Atomic. The conversation includes key considerations for making a business VC backable, understanding total addressable market size, the importance of trust in entrepreneurship, and tips on identifying your strengths and complementing them with the right team. Healey also emphasizes continuous learning and practical financial knowledge for entrepreneurs.

Highlights

00:35 Background and Career Journey

01:43 The Rise of BoomPop

04:02 What Makes a Good Founder?

06:23 VC Backable Ideas vs. Lifestyle Businesses

12:05 Execution and Skill Development for Founders

18:43 Building Trust as a Founder

Healey Cypher is the CEO of BoomPop, and Chief Operating Officer / Partner of Atomic. He has spent his career building and selling companies that center around great customer experience. Healey sold his last two companies: ZIVELO which he sold to Verifone in 2019 after 15 months of taking the reins as CEO, and Oak Labs, which he sold to Zivelo in 2018. Before that, Healey was the Head of Retail Innovation at eBay. Chief of Staff to the CTO of eBay, and lead all business development for milo.com  which he helped sell to ebay in 2010.

He has been recognized as Silicon Valley Business Journal’s “40 under 40,” WWD’s “Ten of Tomorrow,” and one of Goldman Sach’s “Top 100 Most Intriguing Entrepreneurs.” He has also done dozens of keynotes and live TV (most nerve-wracking: CNBC Closing Bell, for sure.)

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Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player.

Want to learn more about us? Head over at https://www.elevano.com

Have questions or want to cover specific topics with our future guests?

Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

In this episode, Willem Koenders, Global Leader in Data Strategy at ZS, joins Amir to unpack how companies can—and should—approach GenAI with realism, not just hype. Willem breaks down the hard truths about legacy data, the prerequisites for AI adoption, and how enterprises must choose between core disruption and co-pilot enhancement. This is a no-fluff, strategic conversation for any tech leader navigating the GenAI wave.

🔑 Key Takeaways:

AI Strategy Starts With Data Reality

Companies must evaluate if their foundational data governance is even ready to support GenAI—shiny tools don't fix bad data.

Legacy Systems Aren’t Excuses—They’re Starting Points

From greenfield rebuilds to domain-driven governance, leaders need a roadmap tailored to their data maturity.

Know Your Role: Core Disruptor or Operational Enhancer

AI’s impact will differ—some industries face existential change, others will gain marginal improvements.

Prep Now, Even If You’re Not Deploying Yet

Build your use case backlog and clean up critical data assets now to accelerate future AI deployment when timing and tools align.

💬 Quote Highlight:

“GenAI tends to make whatever you put in it look elegant—but if the data is bad, the output may be dangerous and you won’t even know it right away.”

⏱️ Timestamped Highlights:

[00:02:00] – Why GenAI is just another tool—and why it still depends on the same old data foundations.

[00:04:30] – Realism vs hype: what GenAI can actually do for your business today.

[00:07:45] – Greenfield strategies vs domain-driven fixes for legacy data challenges.

[00:11:30] – Choosing between disruption and enhancement: how to position AI in your business strategy.

[00:15:30] – Patience is a strategy: when waiting for better tools is the smart move.

[00:20:00] – No-regret moves: how to prep your use cases and data landscape now.

[00:22:30] – Hidden risks: how GenAI can make bad data look deceptively good.

[00:26:30] – Why enterprise AI tools will look very different from consumer-facing tools.

This podcast episode features Matt Robben, Co-founder and CTO of Serif Health, discussing the importance of full-stack engineering hires and how to manage operational growth within hiring constraints. Serif Health, a price transparency data company in healthcare, navigates the challenges of being a small seed-stage startup with a limited hiring budget by prioritizing full-stack capabilities to ensure flexibility and comprehensive problem-solving. Matt highlights the transition from hiring generalist engineers to specialists as the company grows and delves into specific areas like data engineering and artificial intelligence, where specialization becomes critical. He shares insights on identifying talent that showcases eagerness and versatility, underscoring the value of adaptability in the fast-evolving tech landscape. The episode also touches on the interview process, supporting team growth towards specialization, and maintaining a balance between immediate needs and future potential in hiring decisions.

Highlights

00:22 Exploring the Trend Towards Full Stack Engineering

00:44 Introducing Serif Health: A Price Transparency Data Company

01:17 The Strategic Value of Full Stack Engineers in Startups

04:38 The Shift to Specialization: When and Why It Happens

08:20 Navigating Hiring Challenges and Interview Processes

12:31 Fostering Growth and Flexibility Within Engineering Teams

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Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player.

Want to learn more about us? Head over at https://www.elevano.com

Have questions or want to cover specific topics with our future guests?
Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

In this episode, Amir welcomes back Dennis Mortensen — serial entrepreneur, former founder of x.ai, and now CEO of LaunchBrightly — for an unfiltered dive into the emotional highs and lows of entrepreneurship. From building and exiting companies to walking 25km across Manhattan for clarity, Dennis shares what it truly means to love the game of startups.

This isn’t your average "build and exit" story. It’s a deep conversation about why founders keep going, how they process failure, and what it really takes to stay in the arena. Whether you’re building your first SaaS product, pivoting after a setback, or just trying to keep your team motivated — this one’s for you.

🔑 Key Takeaways:

Startups as a 50-year fund: Entrepreneurship isn’t a one-shot game—it’s a lifelong portfolio of experiments.

The emotional resilience game: Success is less about the idea, more about your ability to handle the mental load.

Pivot with purpose: Pivoting is valid—only if you're still chasing the same pain point.

List of hate > list of ideas: Great startups often begin with a problem you just can’t stand.

Focus beats distraction: Dennis avoids advisory roles and side hustles to stay sharp on his core mission.

Reset rituals matter: Dennis’s 3-hour phone-free walks through Manhattan help him stay grounded and creative.

⏱️ Timestamped Highlights:

[00:00:00] – Catching up with Dennis and his new startup, LaunchBrightly

[00:02:00] – Automating product screenshots: why it matters more than you think

[00:05:00] – The “list of hate”: Dennis’s system for surfacing real startup ideas

[00:08:00] – Startups as skill-based games with mostly unknown rules

[00:10:00] – Life without predefined benchmarks — why it breaks most people

[00:14:00] – Why Dennis loves the zero-to-one chaos (and what that says about him)

[00:18:00] – Managing the founder’s emotional rollercoaster

[00:20:00] – How early fatherhood helped Dennis separate self-worth from business outcomes

[00:24:00] – The rise (and risk) of glorifying entrepreneurship on social media

[00:28:00] – Pivoting: what’s valid and what’s just survival-mode flailing

[00:32:00] – When a startup should die — and why that’s not failure

[00:33:00] – Dennis’s long walks: a no-tech ritual for clarity and sanity

[00:35:00] – How to connect with Dennis (and maybe join him on a Manhattan walk)

💬 Featured Quote:

“You should separate your self-worth from the sport you’re playing. I might not have won this race, but I’m as valuable today as I was yesterday.” — Dennis Mortensen

This episode features a detailed discussion with Akshay Swaminathan, a data scientist with significant contributions to health systems improvement, including 40 peer-reviewed publications and features in the New York Times. Currently an AI Researcher, medical student, and PhD candidate at Stanford University, Swaminathan discusses the potential of data science in healthcare, emphasizing the importance of delivering the right treatment to the right patient at the right time. The conversation covers domain specificity in data science, the trend of healthcare professionals engaging in data science, the importance of cross-functional teams, and the role of traditional IT and data science projects in healthcare. Swaminathan also introduces his book, 'Winning with Data Science,' designed to help non-technical domain experts collaborate effectively with data scientists. The episode highlights the potential obstacles to data-driven healthcare solutions, including data quality, biases, interoperability, and data sharing, and suggests starting with identifying key problems before applying data science solutions.

Highlights:

00:38 The Power of Data in Healthcare Delivery

02:03 The Unique Blend of Healthcare and Data Science Expertise

02:52 Cross-Functional Teams: A Trend in Healthcare Data Science

04:56 Empowering Domain Experts to Leverage Data Science

10:35 Case Study: Improving Crisis Response Times at Cerebral

13:30 The Future of AI in Healthcare: Automation and Co-Pilot Solutions

17:41 Challenges and Solutions for Data in Healthcare AI

24:09 Practical Advice for Leveraging Data in Healthcare

Guest:

.Akshay Swaminathan is a data scientist who works on strengthening health systems. He has more than forty peer-reviewed publications, and his work has been featured in the New York Times and STAT. Previously, he was a data scientist at Flatiron Health and Head of Data Science at Cerebral. He is currently an AI researcher and MD/PhD candidate at Stanford University.

LinkedIn: https://www.linkedin.com/in/akshay-swaminathan-68286b51

Book: https://cup.columbia.edu/book/winning-with-data-science/9780231206860

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Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player.

Want to learn more about us? Head over at https://www.elevano.com

Have questions or want to cover specific topics with our future guests?

Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

In this episode, Amir sits down with Zach LaValley, EVP of Engineering at RapidSOS, to explore the high-stakes world of emergency response technology. Zach unpacks how RapidSOS acts as a digital bridge between 911 call centers and a growing network of connected devices—from smartphones to IoT sensors—delivering real-time, life-saving data when seconds matter most.

They dive into the fragmented landscape of U.S. emergency infrastructure, the challenges of routing data across 6,000+ decentralized call centers, and the engineering trade-offs between speed, reliability, and privacy. Zach shares how his team builds for fault tolerance at scale, the unique testing environments required for mission-critical systems, and where AI fits into both the practical and futuristic sides of public safety—whether it’s reducing false alarms or enhancing school security with real-time video analysis.

It’s a behind-the-scenes look at building technology that literally saves lives—and how to do it without compromising speed or trust.

🔑 Key Takeaways:

911 Tech Isn't Centralized: The U.S. emergency system includes 6,000+ decentralized call centers. Routing data accurately and fast is a major engineering challenge.

Latency Can Cost Lives: RapidSOS aims to be faster than the ring of a phone call—delivering life-saving data to emergency services before the call connects.

Privacy with Purpose: The system only pulls data “just in time” to avoid unnecessary storage and protect user privacy.

AI’s Role in Public Safety: While AI isn’t replacing humans, it’s helping filter noise (e.g., false alarms) and power applications like gun detection in schools.

🕒 Timestamped Highlights:

[00:01:00] – How RapidSOS routes real-time data to 911, evolving from GPS to IoT.

[00:04:00] – The complexity of mapping 6,000 call centers and matching jurisdiction with GIS.

[00:06:30] – The challenge of determining the “right” device data in multi-device emergencies.

[00:10:00] – Balancing data access with privacy; no bulk data storage—only on-demand pulls.

[00:12:00] – Why RapidSOS measures performance against telephony speed (must beat the ring).

[00:15:00] – Testing under high stakes: real-world simulation, sandboxes with Apple/Google, canary deployments.

[00:18:00] – Two buckets for AI: offloading non-emergencies and real-time school safety response.

[00:22:30] – Public adoption: Why humans still need to make final calls even in AI-enhanced workflows.

💬 Quote:

“We are trying to save lives with the game of telephone. But the future is in expressive data—pictures, images—that actually show what's happening.” — Zach LaValley

In this episode, Amir Bormand sits down with Harrison Tang, CEO and Co-founder of Spokeo, to explore a problem most people in AI, data, and digital identity overlook: entity resolution. Harrison unpacks how billions of fragmented data records are connected, how we determine what's true in a world of generated content, and why trust and privacy are becoming the new battlegrounds in tech.

They discuss the philosophical foundations of identity, the technical challenges of resolving entities at scale, and how GenAI complicates truth detection. If you're building in data, trust, or anything AI-related—this is required listening.

🧠 Key Takeaways:

Entity resolution is foundational to how we understand digital identity—but it’s far from solved, especially with GenAI-generated noise increasing.

Spokeo resolves 600M+ entities from 19B+ records, using distributed computing and multiple “criteria of truth” (consensus, authority, coherence, etc.).

Generative AI can create content—but not verify it. It’s great for mock/test data, but not for discerning truth.

The real challenge? Detecting fake content. Harrison breaks down the four pillars: provenance, detection, governance, and education.

Privacy ≠ Security. Identity and access management sits above entity resolution, and is crucial for enforcing data control.

⏱️ Timestamped Highlights:

00:55 – What Spokeo does and the scale of its data

02:10 – What is entity resolution? Why it matters

04:10 – The challenge of 19B record comparisons

06:00 – Garbage in, garbage out: why data quality starts at ingestion

07:10 – The five criteria of truth: consensus, authority, consistency, coherence, correspondence

10:40 – Where GenAI helps (and fails) in entity resolution

13:00 – Can AI discern truth like a human? Harrison’s take on AGI skepticism

16:20 – The rise of fake data and the opportunity for Spokeo

18:15 – AI provenance, invisible watermarks, and content authenticity

21:00 – The four pillars of trust in the AI age

23:00 – How privacy impacts data workflows and IAM

25:30 – Why entity resolution sits at the foundation of identity systems

💬 Quote of the Episode:

“The problem of who we are has existed since the beginning of the human race. And in the digital world, that question is more important than ever.” — Harrison Tang

🔗 Resources Mentioned:

W3C Credentials Community Group – where Spokeo contributes on decentralized identity standards

Adobe Content Authenticity Initiative – cited as a tool for detecting AI-generated content

Zero-shot prompting – the concept behind GenAI generating realistic data from a single prompt

🎯 Career Tips (from the episode):

While there wasn’t a dedicated segment on careers, Harrison did hint at a big opportunity area:

If you're in data or security, AI-generated fake content is a growing risk—and a career edge for anyone working on provenance, detection, and digital trust systems.

This podcast episode features Dr. Andrea Isoni, the Chief AI Officer at AI Technologies, discussing AI's current and future integration into everyday life and the corporate world. Isoni highlights how AI already serves as a co-pilot in many aspects of daily activities and how it is adopted across various industries like cybersecurity and manufacturing automation. He also touches on the media's impact on AI perception and the potential for more complex, integrated solutions. The conversation covers the importance of AI in enhancing efficiency and the critical role of Cybersecurity as AI adoption increases. Isoni emphasizes that while AI adoption in enterprises may be slower due to legacy systems and regulations, the consumer market quickly embraces AI tools. He also discusses the evolution of software development, predicting a shift towards more integration, customization, and assurance roles rather than traditional programming.

Highlights

02:06 The Current State of AI Adoption

03:26 AI in Everyday Life and Future Technologies

07:05 AI's Impact on Industries and Consumer Adoption

10:51 Evaluating ROI in AI Projects

14:49 Adoption Challenges and Trust in AI

26:32 The Future of AI, Cybersecurity, and Job Evolution

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Thank you so much for checking out this episode of The Tech Trek. We would appreciate it if you would take a minute to rate and review us on your favorite podcast player.

Want to learn more about us? Head over at https://www.elevano.com

Have questions or want to cover specific topics with our future guests?

Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

In this episode, Nir Kaldero, Chief Data & AI Officer at NEORIS, breaks down what it really takes for enterprises to adopt AI effectively. From selecting the right pilot projects to ensuring leadership buy-in, Nir shares his proven framework—Speed to Value—for driving meaningful AI outcomes. He also highlights the most common mistakes enterprises make and offers strategic advice on aligning AI initiatives with top business goals.

🔑 Key Takeaways

AI = Brain Helper: Nir describes AI as an assistant to human cognition—especially valuable in a data-saturated world.

Speed to Value Framework: Success in AI initiatives depends on four key factors—Sponsorship, ROI, Business Readiness, and Technical Feasibility.

Pilot Smarter, Not Just Faster: Begin AI projects in areas with high potential business impact and strong leadership support.

Change Management is Crucial: Adoption is more cultural than technical—embedding AI into existing workflows matters more than just building flashy tools.

GenAI ROI Misconceptions: Real savings and value lie not just in chatbots but in automation and integration into existing systems.

🕒 Timestamped Highlights

[00:01:00] – NEORIS’s role in enterprise AI transformation

[00:03:00] – Why AI is a “brain helper” in the age of overwhelming data

[00:06:00] – Speed to Value: 4-part framework for prioritizing AI projects

[00:10:00] – Why ROI calculations must involve both business and tech sides

[00:14:00] – Common enterprise mistakes: chasing hype, skipping integration

[00:17:00] – Embedding GenAI into existing dashboards vs. building new tools

[00:23:00] – Why voice and conversational UI may define future UX

[00:26:00] – How to align AI efforts with top-down business strategies

[00:29:00] – Personal reflection: why technologists must stay business-focused

📚 Resources Mentioned

Speed to Value Framework (Sponsorship, ROI, Business Readiness, Technical Feasibility)

💼 Career Tip

"Building technology is easier than changing people's minds."

AI adoption requires top-down sponsorship and must be aligned with business priorities. Technologists who want to make a real impact need to understand—and start from—the business strategy.

💬 Quote

“We can build great models, but people are the ones that change the world. If they don’t adopt the tech, nothing changes.”

In this episode, Amir Bormand sits down with Shashank Tiwari, Co-founder and CEO of uno.ai, to explore how AI is shaping the cybersecurity industry. They dive into the massive skills gap, the role of AI co-pilots, and how security teams can leverage AI to increase efficiency while reducing burnout.

If you’re in tech, security, or AI, this episode unpacks the real-world applications of AI in cybersecurity, the challenges of adoption, and what the future holds for this evolving space.

🎯 Key Takeaways

⏱ Timestamped Highlights

🕒 [00:01:00] – What is uno.ai? Shashank explains their AI-powered cybersecurity co-pilot.

🕒 [00:03:30] – Why is hiring in cybersecurity so hard? The skills gap and industry challenges.

🕒 [00:06:45] – How AI can assist cybersecurity teams – from automation to decision support.

🕒 [00:10:00] – Challenges in adopting AI for security – trust, hallucinations, and enterprise fears.

🕒 [00:14:30] – Will AI change skill requirements in security? The evolving role of security professionals.

🕒 [00:18:00] – How CISOs evaluate AI security tools – proving value and securing AI systems.

🕒 [00:21:30] – The importance of UI/UX in AI adoption – how experience shapes effectiveness.

🕒 [00:24:00] – Future trends in AI-powered cybersecurity.

💡 Notable Quote

"AI is not here to replace cybersecurity professionals—it’s here to empower them by automating the mundane and letting humans focus on the critical." – Shashank Tiwari

📢 Connect with Shashank Tiwari

🔗 LinkedIn: Tshanky

🔗 Website: uno.ai

📧 Email: [email protected]

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