What's Up with Tech?

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If your contact center only finds problems after customers complain, you are operating blind. We sit down with Klearcom at Enterprise Connect to unpack what “CX end-to-end assurance” really looks like when you have voice, IVR, chat, SMS, and now AI-driven journeys all stitched together across multiple regions and vendors.

We get specific about the pain points teams keep running into: manual testing that is slow and subjective, migrations from legacy platforms to CCaaS that introduce silent failures, and the growing need to validate experiences before changes hit production. Klearcom explains a non-intrusive approach to contact center testing and service assurance so you can spot routing issues, latency, and broken paths early, then measure performance like a repeatable confidence metric instead of a gut feeling.

AI is the turning point. Everyone talks about what chatbots and conversational AI can improve, but far fewer talk about how to test them. We dig into chatbot testing that checks accuracy, brand guardrails, structured and unstructured conversations, and the moment that matters most: when a customer asks for an agent, how quickly do they actually get there, and does it meet your SLA? You will also hear how SaaS on AWS supports global teams while handling data residency needs, and why industries with high downtime costs test on a tight cadence.

If you care about customer experience, contact center reliability, and testing AI before it tests your reputation, this one is for you. Subscribe, share with a teammate who owns CX or CCaaS migration, and leave a review, then tell us: what would you automate and test first in your customer journey?
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AI is everywhere right now, but the hard part is turning dozens of pilots into durable systems that actually run a business. We sit down with Tom, SVP of Product Management at GTT, to unpack what changes when enterprises move from “testing AI” to deploying agentic AI at scale across thousands of sites, users, and applications.

We start with how GTT thinks about networking and security as a service, and why the promise is not just bandwidth or a product SKU but a simpler experience that helps customers connect, secure, and simplify. Tom explains the Envision platform and how it spans the edge, the core IP backbone, and public cloud so teams can deliver consistent connectivity, SD-WAN, and security outcomes while preparing for new AI workloads that increasingly want compute closer to the premises.

Then we get practical about what agentic AI requires: data readiness, trustworthy context, and APIs that let agents act safely without constant human validation. We talk frameworks versus one-time deployments, why vendor lock-in is riskier in a fast-changing AI cycle, and how an “AI factory” mindset brings manufacturing discipline to data pipelines, orchestration, validation, deployment, and continuous improvement.

We also share real internal examples, including a cash application agent that helps match remittances to invoices across messy real-world variations, plus how GPU infrastructure supports operational intelligence and proactive network issue detection. If you care about enterprise AI, SASE and SSE, edge computing, and building a scalable agentic architecture, this conversation is built for you. Subscribe, share with a teammate, and leave a review, what part of your AI foundation needs the most work right now?
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Most AI teams are learning the hard way that dumping more text into a prompt does not guarantee better answers. We sit down with Philip Rathle 
Chief Technology Officer from Neo4j to talk about the missing ingredient: relationships. When your data is inherently connected, a graph database can turn scattered facts into usable context, so LLMs and agentic AI systems can respond with more precision and less noise.

We walk through why graph technology is showing up as a “quiet power layer” behind enterprise AI, from knowledge graphs and digital twins to metadata, lineage, and even relationships between vector chunks for graph RAG. Philip explains the practical difference between raw data and knowledge, why multi-hop reasoning matters in domains like financial services and supply chain, and how an AI system can delegate deterministic parts of a problem to a graph while the model focuses on language and judgment.

We also get specific about engineering tradeoffs: why relational databases struggle with constant schema changes, what index-free adjacency means for performance, and how graph queries can run 100x to 2000x faster with less hardware for deeply connected questions. Then we look ahead at where the category is going, including why “graph as a bolt-on feature” often misses the real benefits, plus a roadmap update on Infinigraph for scaling graphs into the 100+ terabyte range. Finally, we cover how AI is making graph adoption easier by inferring graph models from relational sources and helping teams write Cypher queries quickly.

If you’re building enterprise AI, graph RAG, or agentic workflows and you care about accuracy, context, and causality, this conversation will sharpen your architecture instincts. Subscribe, share this with a builder on your team, and leave a review. What’s the hardest connected-data problem you want AI to solve?
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What if one install could fix coverage and unlock an entire layer of building intelligence? We sit down with Michiel Lotter of Nextivity at Mobile World Congress to explore how modern enterprise DAS has evolved from “make the bars appear” to a multi-use platform that delivers public cellular, plug‑in private 5G, and sensor-driven insights across the same footprint.

We break down the Do More with DAS philosophy and why it changes the ROI math for facilities that once viewed in-building coverage as a reluctant expense. Mikhail explains how Nextivity’s system lets teams map antennas to solve dead zones, then mirror that design for private networking by simply connecting a 5G signal source. From there, smart server antennas act as nodes for panic buttons, leak detectors, asset tracking, and environmental sensors, streaming data to an on‑prem edge compute node for fast, secure action.

You’ll hear concrete examples: big box retailers lighting up service in under a week with overnight work; hotel-casino operators meeting staff safety mandates while stopping costly water damage; aged care facilities giving residents dependable connectivity and instant alerts; and hospitals turning their coverage grid into an acoustic awareness network. With embedded microphones and lightweight AI in each antenna, teams can measure noise levels across wards, improving patient experience without deploying a separate sensor mesh.

Along the way, we touch on global carrier approvals, a channel-first approach with system integrators, and the operational win of controlling timelines instead of waiting on lengthy builds. If you care about enterprise connectivity, private 5G, and practical building intelligence, this conversation shows how a single DAS can become the backbone for communications, safety, and analytics.

Subscribe for more deep dives into real-world connectivity, share this with a colleague wrestling with in-building coverage, and leave a review to tell us which multi-use DAS application you’d deploy first.
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What if the fastest way to fix American healthcare is the simplest: put people back at the center. Sitting down with Gil Bashe—chair of Health and Purpose at Finn Partners, editor of Medical Life, and author of Healing the Sick Care System: Why People Matter—we unpack the paradox of a nation that spends more, trains the best, and still delivers poorer outcomes than its peers. Gil brings hard numbers, lived stories, and a clear blueprint for moving from fragmented “sick care” to coordinated care grounded in relationships and results.

We explore the rising frustration across every corner of the system: patients feeling sidelined by delays and denials, clinicians burning out under prior auth and paperwork, payers caught between risk and regulation, and innovators struggling to communicate value. Gil argues for a mission-first mindset where business supports care, not the other way around. He shares how continuity of care, team-based primary care, transparent data-sharing, and measurement that values function and time-to-therapy can rebuild trust while bending the cost curve.

With a global lens—from integrated systems like Clalit to policy lessons shaped by his work across countries—Gil highlights what the U.S. can adapt without importing entire models. We also get tactical on artificial intelligence: where AI can reduce cognitive load, triage more effectively, and streamline documentation, and where governance, bias checks, and explainability must anchor deployment. Along the way, Gil previews the growing bookshelf of patient-centered reform and reads a striking paragraph from his book that reframes health as mission plus discipline.

If you’re hungry for practical hope, this conversation delivers clear steps and a human compass: pay for relationships, simplify the rules, open the data, and design technology that frees clinicians to care. Subscribe, share with a colleague who needs a lift, and leave a review with one change you’d make tomorrow—what would put people first where you work?
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