The Right Answer Isn't Enough—with Karthik Narayan (Komodo Health)

The Right Answer Isn't Enough—with Karthik Narayan (Komodo Health)

If an AI agent gives you the correct answer but took the wrong path to get there, can you actually trust it? In this episode of Futureproof, Prakash Chandran sits down with Karthik Narayan, Director of Product Management at Komodo Health, where he leads Marmot, an enterprise AI product for life sciences. Marmot's promise is that life sciences companies no longer need to send their data to McKinsey and wait months for an answer—they can ask complex healthcare questions and get answers directly. The catch? The answers aren't binary. Together, Karthik and Prakash unpack why grading an agent on whether it got the right answer is only half the story, how Komodo uses parallel critique agents and friction detection to close the gap between AI confidence and analyst rigor, and what changes when AI makes product leaders more powerful than they've ever been. Topics covered include: * Why the path matters more than the answer: How an agent can arrive at the correct number through the wrong query, pass traditional evals, and then fail catastrophically on the next question—and why trajectory evals are the real measure of trustworthiness. * Steering, not just answering: How Marmot uses research plans, follow-up questions, and full code transparency to give analysts maximum control over subjective healthcare methodology decisions. * Friction detection over thumbs up/down: Why users rarely use explicit feedback mechanisms, how Komodo infers dissatisfaction from behavioral patterns, and how that drove a complete platform rewrite at the six-month mark. * Build vs. buy when AI makes prototypes easy: Why a junior engineer's weekend demo isn't the same as a production system with fallback models, context compaction, token optimization, and continuous evaluation—and how to think about total cost of ownership. Episode ID: 19252007-the-right-answer-isn-t-enough-with-karthik-narayan-komodo-health Subscribe to Futureproof wherever you get your podcasts. From Xano - The fastest way to create a production-ready backend for any app or agent. Xano unifies AI speed, code control, and visual clarity, so you never trade reliability for velocity. Sign up for free today. [https://go.xano.co/4oSaKzf]

Foundational Thinking in the Age of AI—with Doug Merritt (Aviatrix)

If AI is making us faster, why does it feel like we're understanding less? In this episode of Futureproof, Prakash Chandran sits down with Doug Merritt, CEO of Aviatrix. Doug is one of the most accomplished enterprise technology leaders of the last two decades—after serving as CEO of Splunk for years, he's now leading Aviatrix to tackle cloud-native security. Together, they unpack why the speed of AI adoption is outrunning foundational understanding, how a recent supply chain attack on the popular LiteLLM framework exposed a massive blind spot in cloud security, and why the leadership principles that matter most right now—curiosity, empathy, and purpose before action—are the same ones our attention-starved culture makes hardest to practice. Topics covered include: * As agents become more human, humans become more binary: Why the speed and abstraction of AI is making our thinking shallower at the exact moment we need it to be deeper—and how to fight back. * The LiteLLM supply chain attack, explained: A breakdown of how attackers injected malware into LiteLLM, harvesting credentials from cloud environments—and why basic egress filtering would have stopped the damage cold. * The three fundamental runtime controls: Why identity, endpoint, and network security are the only controls that actually stop attacks in progress—and why most cloud workloads are missing at least one. * Cloud providers sold speed without brakes: How permissive outbound defaults became the norm, why cloud providers made firewalls an aftermarket add-on, and what that means for every organization deploying AI agents today. * Five leadership principles for the AI age: Doug's hard-won framework—relentless curiosity, leading with empathy, purpose before action, radical accountability, and celebrating success—and why daily mastery beats chasing the next shiny thing. Episode ID: 19139581-foundational-thinking-in-the-age-of-ai-with-doug-merritt-aviatrix Subscribe to Futureproof wherever you get your podcasts. From Xano - The fastest way to create a production-ready backend for any app or agent. Xano unifies AI speed, code control, and visual clarity, so you never trade reliability for velocity. Sign up for free today. [https://go.xano.co/4oSaKzf]

AI Makes Security Everyone's Problem—with Tim Olshansky (Fencer)

If AI agents are writing your code, how are you making sure it's secure? In this episode of Futureproof, Prakash Chandran sits down with Tim Olshansky, CTO and co-founder of Fencer, to explore what application security really looks like in a world where AI writes most of the code and open source software underpins everything. Tim shares his journey from engineer—navigating bureaucratic security processes at larger organizations—to building a platform that makes security accessible for companies under 200 employees. Together, they unpack why compliance certifications often create a false sense of security, how the open source supply chain has become a prime target for attackers, and what "trust but verify" means when Claude is opening your pull requests. They also discuss practical steps any builder can take today—from package manager hygiene to cooldown periods—and why hiring for engineering talent has never been harder to figure out. Topics covered include: * Security as hygiene, not a project: Why treating security like brushing your teeth—small, consistent habits—prevents catastrophic outcomes, and why most small companies still skip it. * The open source supply chain is under attack: How threat actors exploit volunteer-maintained libraries like Axios to gain access to thousands of commercial products at once—and why it's only getting worse. * AI-generated code and the false sense of security: Why LLMs trained on publicly available code don't encode the highest corporate security standards, and why the code itself may not be what gets you hacked. * Trust but verify in an AI-first workflow: How Tim's team moved to nearly 100% AI-driven development while still requiring human review. Episode ID: 19061977-ai-makes-security-everyone-s-problem-with-tim-olshansky-fencer Subscribe to Futureproof wherever you get your podcasts. From Xano - The fastest way to create a production-ready backend for any app or agent. Xano unifies AI speed, code control, and visual clarity, so you never trade reliability for velocity. Sign up for free today. [https://go.xano.co/4oSaKzf]

From Prototype to Enterprise-Grade Compliance—with Michael Konrath (Choice Digital)

What does it really take to build a regulated fintech product from scratch—with a small team and a bootstrapped budget? In this episode of Futureproof, Prakash Chandran sits down with Michael Konrath, co-founder and Chief Product Officer of Choice Digital, a fintech company that ensures customers—including the unbanked—actually receive the payments they're owed. Michael shares the full arc of building Choice Digital: from prototyping on Bubble in a co-working space to processing nearly $1 billion in payments today. Along the way, they dig into the realities of compliance in a regulated industry, the trade-offs of building fast versus building right, and how AI is starting to reshape the way his team ships software. Topics covered include: * Starting small with no-code: Why Choice Digital's first product was built on no-code tools in 30 days, processed $20 million in payments, and lasted far longer than expected—plus how that scrappy mindset still matters in the age of AI. * Compliance as a foundation, not an afterthought: The case for investing in SOC 1 and SOC 2 frameworks early, how PCI compliance shapes product architecture, and why segmenting systems can simplify your regulatory journey. * AI adoption in a regulated space: Why Choice Digital is taking a deliberate, human-in-the-loop approach to AI, focusing on deterministic processes and clear policies before letting models touch customer data. Episode ID: 18983940-from-prototype-to-enterprise-grade-compliance-with-michael-konrath-choice-digital Subscribe to Futureproof wherever you get your podcasts. From Xano - The fastest way to create a production-ready backend for any app or agent. Xano unifies AI speed, code control, and visual clarity, so you never trade reliability for velocity. Sign up for free today. [https://go.xano.co/4oSaKzf]

Dissecting the AI Hype Cycle—with Joshua Greenbaum (Enterprise Applications Consulting)

What if AI is just the latest Blockchain? In this episode of Futureproof, Prakash Chandran sits down with Joshua Greenbaum of Enterprise Applications Consulting to explore the AI hype cycle. Josh reflects on his 30 years of technology consulting and examines whether AI is following the same trajectory as other technologies, where true value can get lost in the froth and frenzy of investors and founders trying to capitalize on it. Together, they explore the reality of whether SaaS really is dead, the criticality of standardizing data in the AI era, and the central question that all AI companies should be able to answer.  Topics covered include: * Technology history repeats itself. While technology itself changes constantly, the way it is received in the market doesn’t. From dotcom to Blockchain, the hype cycle has a pattern. * The hard parts of SaaS. SaaS is certainly changing, but reports of its death have been greatly exaggerated. AI that can build prototypes is far from replacing companies like Salesforce, which have learned from years of on-the-ground work with real customers and real problems. * The importance of data standardization. The value of AI will come down to how well it can access the information it needs. Standardization of data and logic is critical to this outcome, and one that companies have to get right before they can succeed with AI. * The lone wolf developer. Developers aren’t going away, but developers that work in a vacuum may be. Building is a team sport even more than it was before, and the developers who can see and understand business problems are the ones that will build the future. * The real question all AI companies need to ask. No business leader is waking up in the middle of the night thinking, “I need a large language model!” All companies, and AI companies in particular, must remain laser-focused on the actually important question: “What business problem am I solving?” Episode ID: 18906617-dissecting-the-ai-hype-cycle-with-joshua-greenbaum-enterprise-applications-consulting Subscribe to Futureproof wherever you get your podcasts. From Xano - The fastest way to create a production-ready backend for any app or agent. Xano unifies AI speed, code control, and visual clarity, so you never trade reliability for velocity. Sign up for free today. [https://go.xano.co/4oSaKzf]