Guest: Viraj Narayanan, CEO of Cornerstone AI

🔑 Key Takeaways

Healthcare data is messy by default. It's generated by countless sources with different standards—think EMRs, Apple Watches, and pharmacy systems—making research data fragmented and hard to use.

AI can clean up the mess. Cornerstone AI applies automation to standardize and improve the fidelity of clinical research data, significantly cutting down manual effort.

Productivity > Replacement. Rather than replacing jobs, AI is helping PhDs and data scientists focus on higher-value tasks, enabling more research and faster discovery.

Standardization is foundational. Without clean, consistent data, the insights drawn—even with AI—are limited or flawed.

Trust is earned. The biggest mindset shift is seeing your own messy data cleaned instantly by AI, not a polished demo set.

Patients win too. Cleaner, faster data means more reliable research, potentially more personalized medicine, and better access to understandable information.

💬 Quote of the Episode

“We’re going to look back in 10 years and think—‘I can’t believe we had PhDs doing that kind of manual data work.’”

— Viraj Narayanan

⏱ Timestamped Highlights

00:00 – Intro to Viraj and Cornerstone AI: Automating healthcare data quality

01:54 – The "plumbing problem" of healthcare data and what no one thinks about

04:48 – Why AI in healthcare often starts with admin—not research

05:35 – Steph Curry and SNOMED: How basketball shows us the need for standardization

08:58 – Wild West of research data: From 2% lift to 40%+ with AI

11:41 – Why research is built on redundancy and how AI rewires the model

14:43 – Change management: From trust to technical buy-in to leadership alignment

18:42 – Will AI take jobs? No—but it will transform what we do with talent

21:03 – What patients will see: Cleaner, faster, more understandable data

23:49 – Where to reach Viraj and final thoughts

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On this episode of The Tech Trek, we're diving deep into the intersection of engineering, product, and business thinking with Vineet Goel — Co-Founder and Chief Product & Technology Officer at Parafin, a fast-growing fintech startup powering small businesses on platforms like DoorDash, Amazon, and Walmart.

We unpack what it really means to build a company where engineers are product thinkers, why bringing in product managers too early can backfire, and how AI is reshaping what it means to write code — and who’s best positioned to thrive in this new world.

Vineet shares how Parafin scaled with just two PMs to 25 engineers, why every engineer shadows customer support calls, and how GenAI might collapse the wall between product and engineering entirely.

Whether you're an engineer, product leader, founder, or just curious where the future of tech orgs is headed — this conversation is packed with insights you won’t want to miss.

🧠 Key Takeaways

Don’t hire PMs too early. Founders should own product-market fit before bringing on a product leader.

Engineers need a business mindset. At Parafin, engineers are ruthlessly customer-focused — many even shadow support calls.

GenAI will change everything. Writing code is becoming a commodity. Future engineers will need to blend product and technical skills.

The product org evolves with scale. Vineet shares when and why Parafin added a Head of Product, and how it shifted org dynamics.

PMs should create leverage, not just roadmaps. When engineers are stretched thin, PMs help teams stay focused and effective.

⏱️ Timestamped Highlights

00:46 – What is Parafin?

A fintech startup empowering small businesses on platforms like Amazon and DoorDash with embedded financial services.

02:35 – Org Design at Parafin

Why they built a structure that’s neither product- nor engineering-led, but customer-obsessed.

05:09 – 25 Engineers, 2 PMs

How a product-minded engineering culture powers massive output and scale.

06:40 – Customer Empathy as Culture

Engineers shadow support calls—and sometimes ship fixes within the hour.

08:50 – When to Hire a Head of Product

What prompted the shift, and how it solved growing pains around complexity and speed.

11:59 – PMs Create Leverage

Bringing in PMs at the right time accelerates decision-making and keeps engineers focused.

14:28 – Dual Hat of CPTO

How Vineet balances strategy, execution, and organizational leadership.

16:34 – GenAI’s Impact on Engineers

Code is getting commoditized. Engineers must evolve—or risk becoming obsolete.

19:14 – What Happens to Product Roadmaps?

AI will speed up delivery—product teams need to dream further ahead, faster.

21:11 – The ‘Shift Left’ of Engineering

Engineers are moving closer to the business—Vineet predicts a product-tech hybrid role will dominate.

💬 Quote Worth Sharing

“Being product and business minded will become a necessity—not a nice to have. Code is becoming a commodity. The future belongs to those who can build and think.”

— Vineet Goel, CPTO at Parafin

In this episode, Amir sits down with Meg Henry, Head of People & Talent at Companyon Ventures, to unpack a critical—yet often overlooked—aspect of growing technical teams: onboarding.

Engineering leaders spend weeks hiring top talent, only to fumble the first 90 days. Meg shares a tactical, startup-friendly approach to onboarding that actually helps new hires ramp faster, become productive sooner, and stick around longer. If you’ve ever onboarded a dev by tossing them a laptop and saying "Good luck," this one’s for you.

🗝️ Key Takeaways for Tech Leaders:

Weak onboarding kills productivity. Even A+ hires won’t thrive if they don’t know how to succeed.

You’re losing time, not saving it. A 30-minute onboarding plan can prevent months of confusion.

Hybrid makes things harder. Without structure, async teams sink.

Consistency beats chaos. No two roles are the same, but every new hire should feel supported.

AI can help you scale onboarding. Especially when documentation is scattered across Slack, Notion, and Drive.

🕒 Timestamped Highlights:

[00:02:00] Why startups obsess over hiring—but ignore onboarding

[00:04:30] That awkward new hire phase, and how to design around it

[00:05:45] Hybrid onboarding: Why access > answers

[00:07:15] The two onboarding tracks every company needs: company-wide + role-specific

[00:09:30] Founders want plug-and-play hires—but that doesn’t work without a plan

[00:10:45] "Here’s your map": how tech leads can shortcut the ramp-up curve

[00:13:30] Using ChatGPT to build lightweight onboarding flows? Yes, here’s how

[00:15:45] Spotting weak onboarding when you inherit a team

[00:18:15] Customization vs. consistency: how much is too much?

[00:20:00] Time investment: just 2.5 hours over 3 months

💬 Quote of the Episode:

“Before GPS, you wouldn’t invite someone over and just say, ‘Figure out how to get here.’ Even your most autonomous hires need directions.” — Meg Henry

📬 Connect with Meg:

Meg’s helping early-stage B2B startups scale smarter. Connect with her on LinkedIn (Meg Henry, Companyon Ventures) and ask for her free onboarding template—it’s lightweight, practical, and startup-tested.

In this episode, Carlos Peralta returns to The Tech Trek to dive deep into data culture in the wearable tech space, sharing how WHOOP turns petabytes of real-time biometric data into personalized, actionable insights. We explore the technical complexities behind data ingestion, transformation, and delivery, and how the mission-driven nature of WHOOP influences both their engineering decisions and company culture.

🔑 Key Takeaways

Wearable tech = real-time big data: WHOOP processes petabytes of multimodal data from edge devices to deliver insights to users in near real time.

Data must be actionable, not just abundant: It's not about the quantity of data collected, but how that data is translated into meaningful guidance for users.

ML Ops is central to product success: The data and ML infrastructure team plays a critical role in feature development, roadmap planning, and performance optimization.

Mission fuels motivation: WHOOP’s internal culture is deeply driven by its impact on human performance—employees are often users of the product themselves.

Scalability ≠ just growth: Cost-efficiency, forecasting, and cloud infrastructure readiness are vital to scaling responsibly in a global market.

⏱️ Timestamped Highlights

00:00 – Intro to Carlos & the mission behind WHOOP

02:19 – Data culture at WHOOP vs. traditional companies

04:15 – Scale of data in wearables: petabytes, not megabytes

05:52 – Complexity of ingesting, transforming, and delivering personalized data

08:53 – Striking a balance: Real-time feedback vs. cloud cost efficiency

11:14 – Scaling the platform as the member base expands globally

13:43 – Internal motivation and culture driven by positive impact stories

15:56 – Why data teams are involved early in the product roadmap

17:59 – Carlos’ journey from WHOOP user to WHOOP employee

20:40 – How to connect with Carlos + final thoughts

💬 Quote of the Episode

“You can have petabytes of data, but if you can’t make it queriable, understandable, and actionable—it’s just noise.” — Carlos Peralta

In this episode of The Tech Trek, Amir Bormand sits down with Max Mergenthaler-Canseco, CEO and co-founder of Nixla, to explore the nuanced reality behind startup success. A multi-time founder with experience as both CEO and CTO, Max shares hard-earned lessons from his entrepreneurial journey—including why theoretical knowledge often clashes with real-world execution, how to build a resilient startup team, and the underestimated danger of survivorship bias in startup lore.

From balancing optimism with statistical failure rates to knowing when to focus on strengths over weaknesses, Max delivers practical wisdom for anyone navigating the startup grind. Whether you're a first-time founder or on your third venture, this conversation will leave you thinking differently about what it really takes to succeed in tech.

🔑 Key Takeaways

Experience is not a blueprint, it's a lens. Max breaks down how startup learnings aren’t always repeatable but instead shape the founder’s decision-making over time.

Passion is the sustainability engine. You have to love what you're building, not just what the market wants—otherwise, you won’t last through the inevitable startup grind.

Founders vs. early employees. Understanding the difference in motivation and expectations is crucial to building and managing a startup team effectively.

Survivorship bias is everywhere. Max cautions against building a startup playbook based only on outlier success stories.

Know your lane. Instead of leveling up all weaknesses, focus on doubling down where your strengths make the biggest impact.

⏱️ Timestamped Highlights

00:44 – What is Nixla?

Max introduces his company, a time series forecasting and anomaly detection startup with deep roots in research.

01:34 – Serial founder life

Max gives a quick snapshot of his startup journey, from NLP experiments to YC-backed fintech.

03:21 – Startup experience ≠ shortcut to success

Why practical experience matters more than theoretical frameworks, and how each startup is its own universe.

07:59 – Playing the startup game because you love it

Max explains why loving the problem you’re solving is essential for long-term survival and sanity.

10:53 – Hiring the right people early

What Max looks for in early-stage team members—and why founders shouldn't expect employees to grind the same way they do.

13:24 – CEO vs. CTO: Vision vs. Execution

A thoughtful breakdown of the distinct roles and responsibilities between CEO and CTO, especially in early-stage companies.

16:27 – Strengths over Weaknesses

Why Max believes in focusing on what you do well, rather than fixing every flaw.

20:25 – The trap of survivorship bias

A fascinating conversation about how the startup ecosystem overemphasizes success stories and ignores the valuable lessons of failure.

How to reach Max

LinkedIn: https://www.linkedin.com/in/mergenthaler/

💬 Featured Quote

“The only way to keep playing the startup game is to actually enjoy the game.” — Max Mergenthaler-Canseco

In this episode, I sit down with Jason Rogers, CEO & Co-Founder of Invary, to explore an unconventional approach to building a cybersecurity startup—leveraging a tech transfer agreement with the NSA. Jason shares his journey of launching a company around licensed technology, the benefits and challenges that come with it, and why runtime system integrity is becoming a crucial factor in modern security strategies.

We also dive into how AI is changing the cybersecurity landscape, the importance of real-time security validation, and how companies can better protect their systems against evolving threats.

Key Takeaways

🔹 Tech transfer provides a competitive edge – Licensing government-developed technology can offer startups a head start with validated, battle-tested IP.

🔹 Security needs to be proactive, not reactive – Real-time validation of system integrity can prevent breaches before they escalate.

🔹 Collaborative research fuels innovation – Invary works with the NSA and academic institutions to advance security capabilities.

🔹 AI is expanding the attack surface – As AI adoption grows, ensuring system and data integrity will be more critical than ever.

🔹 Zero trust applies to machines too – It’s not enough to verify users—organizations must continuously verify their systems.

Timestamped Highlights

⏳ 00:01 – Introduction to Jason Rogers and Invary’s mission

⏳ 00:49 – How NSA-licensed technology is securing critical systems

⏳ 01:36 – The journey from research-backed tech to startup success

⏳ 02:56 – The challenges and benefits of building a business around licensed IP

⏳ 05:32 – Collaborating with government research teams for innovation

⏳ 09:33 – How engineers adapt to the tech transfer model

⏳ 14:06 – Why runtime integrity is the missing piece in security

⏳ 16:34 – The shift from traditional security models to real-time validation

⏳ 19:27 – AI’s growing attack surface and what it means for security

⏳ 23:28 – Predicting future cybersecurity challenges in an AI-driven world

⏳ 24:00 – How to connect with Jason

Quote from the Episode

“The bad guys collaborate all the time. It’s time for the good guys to do the same.” – Jason Rogers

Connect with Jason Rogers

🔗 Website: Invary.com

🔗 LinkedIn: https://www.linkedin.com/in/jasonlrogers/

Stay Connected with The Tech Trek!

🎧 Like what you heard? Subscribe, rate, and review on your favorite podcast platform!

📩 Have feedback or guest suggestions? Connect with Amir on LinkedIn.

🔔 Follow for more deep dives into technology, security, and innovation.

In this episode, Amir sits down with Kaustav Das to discuss one of the most critical yet challenging aspects of analytics—asking the right questions. They explore how analytics leaders can better navigate conversations with stakeholders, ensuring they gather the correct requirements and deliver actionable insights. The conversation touches on the evolving role of analytics, the impact of generative AI in business intelligence, and how decision-making is shifting toward more conversational data engagement.

Key Takeaways

The Power of Asking the Right Questions: The quality of analytics is only as good as the questions being asked. Stakeholders’ intent must be fully understood before diving into solutions.

Balancing Speed with Thoughtfulness: Quoting Einstein, Kaustav highlights the importance of preparation: “If I were to chop a tree down in an hour, I would spend 55 minutes sharpening my blade.” Rushing to a solution without understanding the problem leads to inefficiencies.

Technology vs. Process: Not all business challenges require a technology-driven solution. Often, simpler process optimizations can be more effective.

Conversational Analytics & AI: Generative AI is shaping analytics by making data interactions more intuitive, but expertise in asking the right questions remains critical.

Roadmapping for Success: The PTP (Present-To-Path) framework helps stakeholders clarify their goals, define a roadmap, and create an execution timeline for analytics projects.

The Art vs. Science of Analytics: Analytics is more of an art than a science. Understanding business goals, managing multiple stakeholders, and iterative questioning are key to driving value.

Timestamped Highlights

[00:00] Introduction to the episode and guest, Kaustav Das.

[01:08] Why asking the right questions is critical in analytics.

[04:58] Do technologists jump to solutions too quickly?

[06:01] The balance between planning and execution in a fast-paced environment.

[07:28] The high failure rate of technology projects—why intent matters.

[10:52] The five “whys” technique and getting to the core of business problems.

[12:24] The future of analytics—can it become more conversational?

[17:03] Measuring ROI in marketing and media analytics.

[20:29] Where to connect with Kaustav Das.

Quote of the Episode

"If I were to chop a tree down in an hour, I would spend 55 minutes sharpening my blade." – Albert Einstein, referenced by Kaustav Das

Connect with Kaustav Das

LinkedIn: https://www.linkedin.com/in/kaustavanalytics/

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What happens when you build a business around what you genuinely love? In this episode of The Tech Trek, Amir sits down with Michael Farb, CEO of Boatsetter — the Airbnb of boats — to unpack how passion can be a strategic advantage in tech entrepreneurship.

Michael shares his journey of launching multiple businesses rooted in personal interests, from college sports to global philanthropy to now, outdoor water adventures. Together, they explore what it really takes to turn a personal obsession into a scalable business, how to identify real opportunities in your hobbies, and why solving a specific problem matters more than chasing a massive market.

Whether you're dreaming about launching your own thing or leading product inside a startup, this conversation is packed with insights on product-market fit, customer discovery, and building teams who care as much as you do.

🧠 Key Takeaways

Passion is a superpower: When you’re obsessed with a hobby or space, you naturally develop deep insights others don’t see — and that can unlock serious business potential.

Solving problems > chasing scale: Michael shares how the best businesses often start by solving a very specific problem — even if that solution doesn't scale at first.

Inspiration is everywhere: Whether it’s boats, black cars, or model airplanes, there’s almost always a business idea hiding in what people love to do.

Team alignment is critical: Boatsetter thrives by hiring people who live and breathe outdoor adventure — passion isn't just a founder trait, it's company-wide.

Don’t overthink TAM: Many aspiring founders kill ideas too early worrying about market size. Start small, build value, and the market might grow with you.

⏱️ Timestamps & Highlights

00:00 – Introduction

Michael Farb joins Amir to talk about building businesses around personal passions and how that philosophy led to Boatsetter.

01:00 – What is Boatsetter?

A two-sided marketplace for boat rentals in 700+ global locations. No boating license? No problem.

02:20 – Michael’s Entrepreneurial Journey

From sports recruiting tech to nonprofit fundraising platforms — every business tied back to something he personally cared about.

04:45 – How to See the Business in Your Passion

“If you’re obsessed with a space, you’ll know more than anyone else. That’s your edge.”

08:00 – Advice for Aspiring Passion-Driven Entrepreneurs

Look for friction points in your hobby — that’s where business opportunities are born.

10:50 – Employees with Passion

Boatsetter hires people who love the water. They even get boating credits as part of their benefits.

14:00 – Working with Product Teams as a Passionate CEO

Michael partners closely with product to scale both sides of the marketplace — consumers and boat owners.

16:00 – Would He Ever Build a Business Without Passion?

Short answer: No. The passion + business combo has worked too well to ditch.

19:00 – Do You Need Market Research to Start?

Michael skips the spreadsheets — he talks to real people and builds MVPs to validate problems.

21:30 – “Do Things That Don’t Scale”

The early Boatsetter days were scrappy. Human-powered logistics and manual processes — until the model was proven.

24:40 – How to Connect with Michael

Find him on LinkedIn or visit Boatsetter.com.

💬 Quote to Share

“Don’t get paralyzed trying to figure out how big the market is — just solve a real problem. Everything big started small.” – Michael Farb

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Follow, rate, and share The Tech Trek wherever you get your podcasts. Got feedback or guest suggestions? Hit up Amir on LinkedIn or drop a comment.

In this episode, Zachary Hanif, VP of AI, ML, and Data at Twilio, joins Amir to talk about the engine behind B2B AI innovation. From selecting the right tools to navigating the shift from POCs to production, Zachary offers an insider's look at how enterprises can thoughtfully and effectively integrate AI.

We unpack:

The danger of "boiling the ocean" with AI

Why chatbots aren’t always the right starting point

What makes an AI POC actually valuable

And why UX in the age of AI needs systems thinking

💬 “If you come into it with a technology and not a firm understanding of the problem, you're going to solve a problem that isn’t there — and at best, you'll just end up with a great tech demo.” – Zachary Hanif

🔑 Key Takeaways

Start with the use case, not the tool: Jumping in with LLMs without a clear business problem leads to superficial results.

UX in AI is different: You’re not just designing for humans—you're designing for a human-model-human interaction loop.

POCs must build trust: Especially with generative AI, proof-of-concepts must feel reliable and human-like to succeed.

AI increases surface area: Models introduce new attack surfaces and complexities. Security, observability, and model risk management are critical.

Think systems, not screens: LLMs change how users interact with software. This demands broader thinking from designers and PMs.

⏱️ Timestamped Highlights

00:00 – Intro to Zachary Hanif and Twilio's AI mission

02:05 – Why most companies are AI tool users, not tool makers

04:25 – The “chatbot temptation” and why it might not be the best starting point

06:00 – UX lessons from Google’s early search box vs. today’s LLMs

08:30 – Why we’re still early in discovering transformative AI use cases

11:55 – How AI changes what a good POC looks like

14:59 – Should AI UX be its own discipline?

18:23 – How to know when a POC is ready for production

22:12 – Dealing with AI’s expanding “surface area” and model drift

25:56 – Why model risk management matters more than ever

In this episode, Amir chats with Bobby Touran, the non-technical CEO and co-founder of Rainbow, an insurance tech company that thrives on strong engineering culture. They dive deep into how non-technical founders can effectively collaborate with technical teams, foster a hybrid office culture, and ensure that engineers are closely aligned with business objectives. Bobby shares how Rainbow maintains a tight feedback loop, how returning to in-office work has shaped their growth, and why AI isn’t necessarily replacing jobs in insurance but transforming them.

🔑 Key Takeaways:

Non-technical founders can be powerful allies to engineering teams by focusing on context, communication, and fostering a collaborative culture.

Being back in the office has served as a key differentiator in attracting engineers who value social interaction and cross-functional communication.

Culture is not static—Bobby emphasizes actively evolving it through regular offsites, tools like Slack Donut, and clear alignment between technical and business teams.

Engineering teams thrive when given real-world context—Rainbow’s practice of dining at insured restaurants is a brilliant example of tying product to user impact.

Transparency and experience matter—Rainbow’s engineering team is senior, engaged in thoughtful discussions on technical debt, and values clear communication around business priorities.

AI is reshaping roles, not removing them—Bobby shares his view that AI will augment rather than replace underwriting and risk assessment functions in insurance.

⏰ Timestamped Highlights:

00:00 – Introduction to Bobby Touran and overview of Rainbow’s mission.

02:10 – Bobby’s approach as a non-technical founder and fostering strong relationships with engineers.

04:43 – Building a healthy engineering org: culture is intentional, not accidental.

07:48 – The importance of engineers understanding the "why" behind product decisions.

10:29 – Why being back in the office twice a week has helped Rainbow’s growth and culture.

15:03 – How engineering and leadership collaborate on roadmap and growth opportunities.

19:03 – Decision-making confidence: balancing business opportunities and engineering cycles.

21:59 – Bobby’s take on AI's role in insurance and how Rainbow is integrating it.

25:43 – Connect with Bobby and explore career opportunities at Rainbow.

💡 Featured Quote:

"The culture is not something that you define and it just is what it is. It’s something you actively work on. It’s always evolving." – Bobby Touran

📢 Connect & Learn More:

Website: useRainbow.com – Check out the About Us page and open engineering roles.

LinkedIn: https://www.linkedin.com/in/bobby-touran-782a6513/

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