Tech Talks Daily

What happens when engineering teams can finally see the business impact of every technical decision they make? In this episode of Tech Talks Daily, I sat down with Chris Cooney, Director of Advocacy at Coralogix, to unpack why observability is no longer just an engineering concern, but a strategic lever for the entire business. Chris joined me fresh from AWS re:Invent, where he had been challenging a long-standing assumption that technical signals like CPU usage, error rates, and logs belong only in engineering silos. Instead, he argues that these signals, when enriched and interpreted correctly, can tell a much more powerful story about revenue loss, customer experience, and competitive advantage. We explored Coralogix's Observability Maturity Model, a four-stage framework that takes organizations from basic telemetry collection through to business-level decision making. Chris shared how many teams stall at measuring engineering health, without ever connecting that data to customer impact or financial outcomes. The conversation became especially tangible when he explained how a single failed checkout log can be enriched with product and pricing data to reveal a bug costing thousands of dollars per day. That shift, from "fix this tech debt" to "fix this issue draining revenue," fundamentally changes how priorities are set across teams. Chris also introduced Oli, Coralogix's AI observability agent, and explained why it is designed as an agent rather than a simple assistant. We talked about how Oli can autonomously investigate issues across logs, metrics, traces, alerts, and dashboards, allowing anyone in the organization to ask questions in plain English and receive actionable insights. From diagnosing a complex SQL injection attempt to surfacing downstream customer impact, Oli represents a move toward democratizing observability data far beyond engineering teams. Throughout our discussion, a clear theme emerged. When technical health is directly tied to business health, observability stops being seen as a cost center and starts becoming a competitive advantage. By giving autonomous engineering teams visibility into real-world impact, organizations can make faster, better decisions, foster innovation, and avoid the blind spots that have cost even well-known brands millions. So if observability still feels like a necessary expense rather than a growth driver in your organization, what would change if every technical signal could be translated into clear business impact, and who would make better decisions if they could finally see that connection? Useful LInks Connect with Chris Cooney Learn more about Coralogix Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.

What does real AI transformation look like when leaders stop chasing prototypes and start demanding outcomes they can actually measure? That question sat at the center of my conversation with Alex Cross, Chief Technology Officer for EMEA at CI&T, alongside Melissa Smith, as we unpacked why so many organizations feel stuck between AI ambition and business reality. There is no shortage of excitement around AI, but there is growing skepticism too, especially from leadership teams who have seen pilots come and go without clear return. This episode focuses on how CI&T is addressing that gap head on. Alex shared how CI&T frames its work as AI-enabled transformation rather than simply layering AI tools onto existing processes. The distinction matters.  Instead of using AI to speed up broken workflows, CI&T reshapes how work gets done so AI becomes part of value creation itself. We explored a standout example from ITAU, the largest bank in Latin America, where deep modernization work helped deliver gains that most executives only ever see in strategy decks.  Productivity rose sharply, digital launch cycles collapsed from years to months, customer satisfaction jumped, and the commercial impact reached hundreds of millions in uplift. These are the kinds of results that change boardroom conversations. A big part of how CI&T gets there is its proprietary Flow platform. Alex explained how Flow gives clients a day-one AI environment, removing the heavy upfront cost and complexity that often slows momentum. Instead of spending months building platforms before any value appears, teams can move from proof of concept to production in as little as six to eight weeks. Flow also plays a second role that many AI programs miss, acting as a measurement layer so performance, efficiency, and ROI are visible rather than assumed. We also talked about why partnerships matter when execution is the goal. CI&T works closely with hyperscalers like AWS and Databricks, combining native tools with its own codified expertise. That combination has helped the company achieve an unusually high success rate in bringing AI initiatives to production, a challenge many organizations still struggle with. For Alex, the difference comes down to a relentless focus on production readiness and collaboration between business and technology teams from day one. Looking ahead, the conversation turned to CI&T's expansion across EMEA and what the company's 30th year represents. Rather than chasing every new trend, the focus is on productizing services around real client problems, whether that is legacy modernization, efficiency, or growth. The goal is to bridge strategy and execution in a way that feels practical, fast, and accountable. If you are leading AI initiatives and wondering why progress feels slower than the hype suggests, this episode offers a grounded perspective from the front lines. So, as organizations head into another year of bold AI plans, the real question becomes this. Are you building faster caterpillars, or are you ready to do the harder work required to turn ambition into something that can truly scale? Useful Links Connect with Alex Cross Connect With Melissa Smith Learn more about CI&T Follow CI&T on LinkedIn and YouTube Thanks to our sponsors, Alcor, for supporting the show.

What does AI-led transformation actually look like when it moves beyond pilots, hype, and slide decks and starts changing how work gets done every day? That question framed my conversation with Venk Korla, CEO of HGS, at a time when many organizations feel both excited and exhausted by AI. Boards want results, teams are buried in proofs of concept, and leaders are under pressure to show progress without breaking trust, budgets, or operations. This episode cuts through that tension and focuses on what it takes to turn ambition into outcomes. Venk shared how HGS thinks about what he calls intelligent experiences, where customer interactions are directly connected to operational follow-through. Instead of treating AI as a front-end layer or a chatbot add-on, HGS links context, data, and fulfillment so the experience continues after the conversation ends. We talked through practical examples, from airlines proactively rebooking stranded passengers before they queue at a desk, to healthcare providers guiding patients step by step before and after surgery with timely, relevant messages. In each case, the value comes from anticipation and execution, not novelty. A big part of our discussion centered on why so many AI initiatives stall. Venk described how organizations often chase technology first, launching pilots without redesigning the underlying process. HGS takes a different route through what they call Realized AI, embedding AI into specific workflows with clear ownership and measurable goals. The focus is on outcomes such as faster processing, higher compliance, and improved customer satisfaction, all proven within a ninety day proof of value. It is a disciplined approach that favors repeatability over experimentation theater. We also spent time on cloud strategy, an area where expectations and reality often collide. Venk was candid about why simple lift-and-shift migrations fail to deliver value. Without re-architecting applications to take advantage of elasticity and serverless compute, cloud spend can grow while performance stalls. He shared how a FinOps mindset, combined with application redesign, helped one client dramatically improve load speeds while reducing costs, reinforcing the idea that transformation requires structural change, not surface movement. Ethics and trust were another thread running through the conversation. Venk emphasized that AI systems are only as reliable as the data, governance, and oversight behind them. Human-in-the-loop design remains central at HGS, ensuring accountability, empathy, and confidence for both customers and employees working alongside AI. This balance between automation and human judgment came up again when we discussed their software-as-a-surface model, where AI and people work together in a carefully orchestrated way, with pricing tied to resolved outcomes rather than activity alone. As the pace of change continues to accelerate, this episode offers a grounded perspective on how to move forward without getting lost in noise. If you are leading transformation and feeling pressure to show progress, the real challenge may not be choosing the right tool, but deciding which outcomes truly matter and redesigning work around them. As AI, cloud, and customer experience continue to converge, are you building systems that look impressive in demos or that deliver predictable results when it counts? Useful Links Connect with Venk Korla Learn more about HGS Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.

What if the biggest breakthrough in weight management is not a new diet, but finally seeing how your body responds in real time? That question sat at the center of my conversation with Sharam Fouladgar-Mercer, CEO and co-founder of Signos, a continuous glucose monitoring (CGM) and AI-powered health platform built to help people manage weight by understanding their metabolism. January is when motivation is high and the wellness noise is loud, but it is also when a lot of people realize how hard it is to stick with generic advice that does not fit real life. This episode is about why personalization matters, how metabolic signals can change the way you think about food and exercise, and what happens when health technology shifts from reporting the past to guiding the next decision. Sharam explained how Signos pairs a CGM with an AI-driven experience that turns glucose data into practical actions. The point is not to force people into rigid rules or extreme restrictions. Instead, it is about learning how your body reacts to everyday choices, then using that feedback to reduce spikes, improve consistency, and build habits you can actually live with. We talked about simple interventions, like changing the order of foods in a meal, timing movement more intelligently, and spotting patterns that would otherwise stay invisible. Two personal stories brought the conversation to life. Sharam shared how he lost 25 pounds while increasing his calorie intake, which challenges a lot of assumptions people carry into weight loss. He also shared a story from his family life, where his wife's deep sleep increased from roughly 20 minutes a night to around 60 minutes after focusing on glucose stability, even while total sleep time remained limited during the intense period of raising young kids. It is the kind of detail that hits home for anyone who has ever tried to make healthier choices while exhausted and stretched thin. We also explored why FDA clearance matters for Signos and what that could mean for mainstream access. Over-the-counter availability reduces friction, can lower cost, and opens the door to broader adoption, including potential FSA and HSA eligibility. Looking ahead, Sharam shared a vision that goes beyond weight management, connecting metabolic health to the long arc of prevention and chronic conditions where insulin resistance plays a role. If you have ever felt like you are doing all the "right" things and still not seeing results, this episode will make you rethink what "right" even means. And if you could finally see your metabolism in real time, would it change how you approach food, sleep, exercise, and the habits you want to keep this year? Useful Links Connect with Sharam Fouladgar-Mercer Learn more about Signos Instagram, Facebook, X and YouTube Thanks to our sponsors, Alcor, for supporting the show.  

What if your website could spot its own problems, fix them, and quietly make more money while you focus on building your business? That question sat at the heart of my conversation with Aviv Frenkel, co-founder and CEO of Moonshot AI, and it speaks to a frustration almost every founder and digital leader recognizes. Traffic is expensive, attention is fragile, and even small issues in design or flow can quietly drain revenue for months before anyone notices. Traditional optimization often means long cycles, internal debates, and teams juggling analytics, design tools, and testing platforms while hoping the next experiment moves the needle. Aviv's perspective is shaped by lived experience. Before building Moonshot AI, he ran an e-commerce company that had plenty of visitors but disappointing conversion. Like many founders, he watched teams guess at fixes, wait weeks for tests to run, then struggle to link effort to outcome. Moonshot AI was born from that frustration, with a simple ambition. Let the website diagnose what is broken, generate solutions, test them, and deploy the winner automatically, without the need for a dedicated growth team. In our discussion, Aviv explained how Moonshot focuses on front-end experience and site performance, spotting issues such as unclear value propositions, poorly placed calls to action, or confusing mobile navigation. The platform generates its own design, copy, and code variants, runs live tests, and then rolls out what actually works. The results are hard to ignore. Brands across beauty, fashion, jewelry, and consumer electronics are seeing revenue per visitor lift by thirty to fifty percent within months. One small change to a mobile navigation menu at Hugh Jewelry led to a fifty seven percent increase in revenue per visitor, which is the kind of outcome that gets leadership teams paying attention. We also talked about momentum behind the company itself. A recently announced ten million dollar seed round has given Moonshot AI the resources to scale engineering and go-to-market teams at a time when demand is accelerating fast. But beyond funding and growth charts, what stood out most was Aviv's longer-term view. As more people turn to AI assistants and agents instead of traditional search, websites need to be structured so machines can understand them as clearly as humans. Moonshot is already optimizing for that future, preparing sites for an agent-driven web where the customer might be an algorithm as much as a person. Aviv also shared his personal journey, moving from a successful career as a tech journalist and TV host into the far more humbling world of building companies. Rejection, uncertainty, and hard lessons came with the territory, but so did clarity. His guiding idea, inspired by Jeff Bezos, is a minimum regret mindset, choosing the harder path now to avoid looking back later and wondering what might have been. So as AI moves from tools that assist to systems that act, and as websites become active participants in growth rather than static assets, the big question becomes this. Are you still relying on slow, manual optimization cycles, or are you ready to let your website start improving itself, and what does that shift mean for how you build and scale in the years ahead? Useful Links Connect with Aviv Frenkel Learn More About Moonshot AI Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.