Inference Analytics AI

From the Inference Analytics AI Lens: Insights from NVIDIA GTC as NVIDIA Hits $5 Trillion and AI Accelerates Forward 🚀 What an absolutely electrifying few days at NVIDIA GTC in Washington, D.C. — one of the most vibrant gatherings in tech today! ⚡ Being surrounded by innovators, researchers, and partners at the forefront of AI, quantum computing, and next-gen connectivity was both humbling and energizing. Standing at the epicenter of innovation with perhaps the world’s most transformative company, I was reminded how fast the AI ecosystem continues to evolve — and how critical collaboration has become in shaping the future. 💚 I’m incredibly proud that Inference Analytics AI Analytics was featured in NVIDIA’s “Coming Age of Agentic Health” showcase, highlighting our work as we continue to scale beyond healthcare and into new industries. Our partnership with NVIDIA is thriving — and it was a privilege to connect in person with their talented, supportive team and our fellow Inception partners. Despite reaching an astonishing $5 trillion valuation, NVIDIA continues to operate with the curiosity, humility, and agility of a startup. That’s not just impressive — it’s inspiring. Some keynote moments that really stood out for me: 🔹 Quantum + GPU Integration: The introduction of NVQlink — bridging quantum computers directly to NVIDIA GPUs — signals a bold new chapter in hybrid computation. The implications for real-world problem-solving are massive. 🔹 The 6G Frontier: NVIDIA ’s partnership with Nokia marks an exciting foray into 6G technology, paving the way for faster, more intelligent, and more connected AI-driven systems across mobile and edge networks. 🔹 Culture & Community: From meeting fellow Inception partners to learning from leaders across industries, the sense of shared purpose was unmistakable. This is a community that builds — together. 💡 At Inference Analytics AI Analytics, we came away more certain than ever that the world needs to accelerate AI development — responsibly, intelligently, and at scale. That’s exactly what our AI Development Platform enables: empowering organizations to rapidly build, adapt, and deploy domain-specific AI systems that deliver measurable business outcomes. As AI evolves from capability to collaboration — from tool to teammate — platforms that drive speed, trust, and specialization will define the next wave of innovation. Here’s to the convergence of AI, quantum, and connectivity — and to building the future together. 🌍✨ #NVIDIA #GTC #InferenceAnalytics #AI #QuantumComputing #6G #HealthcareAI #AgenticHealth #InceptionPartner #Innovation #AIDevelopment #AIPlatform #AIAcceleration, Chelsea Sumner, PharmD, Rph Jack Resnick

🚀 Inference Analytics AI Featured in NVIDIA’s “Coming Age of Agentic Health”. We’re thrilled to share that Inference Analytics AI has been recognized as one of NVIDIA’s named partners in their “Coming Age of Agentic Health” showcase. This recognition highlights our role in shaping the future of agentic AI and secure intelligent systems in healthcare — where precision, privacy, and performance truly matter. 🤝 A Longstanding Partnership Our journey with NVIDIA goes back to the Biomegatron days — when we were building some of the earliest large-scale, domain-specific AI models, about 3 years before ChatGPT. Since then, our collaboration has been grounded in a shared vision: bringing applied, responsible, and domain-aware AI to the most sensitive data environments on Earth. 🧠 Expanding Beyond Health Inference Analytics AI while healthcare remains our foundation, we’re now extending our Agentic + No-Code Development Platform into other industries that demand rigorous data protection (sensitive data industries) — from telecom and financial services to government and regulated enterprise sectors. The next generation of AI isn’t just intelligent — it’s accountable, composable, and agentic. 💬 Meet Me at NVIDIA GTC I am attending NVIDIA GTC in DC this week — if you’re attending and want to talk about the future of Agentic AI, secure model orchestration, or how we’re helping organizations build trust into every layer of intelligence — DM me! #NVIDIA #GTC2025 #AgenticAI #HealthcareAI #NoCodeAI #AIInnovation #InferenceAnalyticsAI #NVIDIAHealth #AppliedAI #LLMs #ResponsibleAI #AIAgents #HealthTech #AIPlatforms #NVIDIAEcosystem #DataSecurity #EnterpriseAI Chelsea Sumner, PharmD, Rph Iris Chenn, Renee Y., Jack Resnick

🚀 Don’t Overthink AI: The Case for Building Before Excessive Planning Most organizations freeze at the starting line of AI. They want to define the perfect strategy — analyze use cases, assess risks, align with business goals, and fit everything into a multi-year roadmap. Sounds sensible, right? Except AI doesn’t reveal its best use cases in a strategy workshop. It reveals them in the wild — when users start interacting, breaking things, and reshaping your assumptions. 💡 The Trap of “Strategic Perfection” Enterprises love structure and control — and that’s where many stumble. They treat AI like another digital transformation project. But AI isn’t linear. It’s emergent. It evolves as you build. By the time a carefully crafted roadmap is approved, the tools and realities have already changed. That’s not strategy — that’s analysis paralysis disguised as prudence. ⚙️ The Alternative: Build. Break. Learn. The most successful AI initiatives we’ve seen at Inference Analytics don’t start with a 100-page plan. They start with a single use case — built fast, tested early, refined through feedback. This “trial by error” model works because AI systems thrive on iteration. You learn faster than you plan. Even when a pilot “fails,” it exposes what matters — 👉 Which workflows truly benefit from AI 👉 Where adoption friction lives 👉 What data or context the model needs to perform That’s insight no consultant can deliver — it comes only through doing. ⚙️ Where No-Code Changes the Game This is why we’ve built no-code AI tools at Inference Analytics AI — to remove friction between idea and implementation. Our platform lets teams: ✅ Prototype AI use cases in days, not months ✅ Gather feedback inside real workflows ✅ Iterate models and prompts without engineering bottlenecks That speed turns experimentation into strategy. When iteration is easy and inexpensive, learning becomes your competitive advantage. 🧭 The Framework: Build to Learn Here’s how to apply it: Start Narrow → Launch one or two quick use cases. Observe & Iterate → Let user feedback guide improvement. Extract Insights → Find adoption and ROI patterns. Scale Smart → Use those lessons to shape your broader AI roadmap. You’re not skipping strategy — you’re letting learning shape strategy. ⚡ The Real Risk Isn’t Failure — It’s Inertia In AI, the cost of being late is higher than the cost of being wrong. Every experiment teaches you something. Every delay teaches you nothing. So instead of asking, “What’s our perfect AI strategy?” Ask, “What can we build this month to learn faster than our competitors?” Because in AI, clarity doesn’t come before you build — It comes because you built.

🚀 From Code to Clicks: Why No-Code AI Is the Future of Enterprise Innovation The AI revolution isn’t just about bigger models or smarter algorithms. It’s about who gets to build and deploy them. For too long, AI has been locked away in the domain of specialists—data scientists, ML engineers, research teams. But if AI is truly going to transform industries, it has to be in the hands of the people who understand the problems best: the practitioners and IT teams inside enterprises. That’s why no-code AI platforms are essential. 🛠️ Breaking the Specialist Bottleneck Traditional AI development requires coding expertise, data science skills, and infrastructure knowledge. This slows down adoption and creates bottlenecks: Business teams depend on technical experts. Iterations take months, not weeks. Innovation is gated by scarce talent. No-code platforms change the equation. They let non-AI builders—clinicians, analysts, operators, and yes, IT teams—directly create and manage AI workflows. 🧮 Why Classification + GenAI Together Matters At Inference Analytics AI, we’ve built AI Studio—a platform that uniquely combines: Classification models (precise, structured, rules-driven) Generative AI models (adaptive, contextual, expressive) This fusion matters because real enterprise use cases require both: A classifier flags what matters (fraud, a clinical anomaly, a compliance breach). A GenAI model explains it, summarizes it, or guides next steps. With AI Studio, enterprises can build repeatable, reusable flows that combine classification + GenAI—without reinventing the wheel for each new use case. 🏥 Training IT Teams to Scale AI One of our large hospital customers is using AI Studio not just to help clinicians, but to train IT teams to become AI builders. IT staff are learning to design repeatable workflows inside AI Studio. Instead of writing custom code for every use case, they build platform-level flows that can be reused across departments. This approach accelerates adoption, reduces costs, and creates a consistent foundation for AI across the organization. AI Studio becomes more than a tool—it becomes the operating system for enterprise AI. 🌍 Real-World Use Cases Across Industries 🏥 Healthcare: Clinicians and IT teams collaborate to flag anomalies and auto-generate clinical summaries. 💰 Finance: Analysts use repeatable fraud detection + GenAI explanation flows for compliance. 📡 Telecom: IT teams build anomaly detection + troubleshooting guides once, then reuse them across regions and networks. 📜 Legal: Lawyers and IT staff deploy clause-checking + GenAI drafting flows that scale across contracts. 🚀 Our Point of View At Inference Analytics AI, we believe the future of enterprise AI won’t be built by a few experts, but by tech teams and AI enthusiasts everywhere. That’s why we built AI Studio—to make building AI simple, repeatable, and scalable. 👉 Interested in trying it out? Reach out—we’d love to connect.

🚀 The AI You Can Trust: How High-Stakes Expertise Drives Cross-Industry Value In today's fast-paced AI world, everyone's talking about the next big thing. But what truly matters for your business? Trust, accuracy, and proven reliability. These aren't just buzzwords; they're the foundational pillars built in the most demanding environments, now ready to empower your industry. Consider the precision required in healthcare, where AI's ability to generate accurate, context-aware text from complex data is critical. This isn't just about interpreting images; it's about converting intricate findings into clear, hallucination-free reports – a discipline that demands unparalleled rigor. Why Healthcare-Honed AI is Your Competitive Edge: The expertise gained from tasks like generating reliable radiology reports as we experienced at Inference Analytics AI has pushed the boundaries of what AI can do for all industries, delivering capabilities in: - 🔍 Unwavering Accuracy: Where errors are unacceptable, AI systems are trained to deliver pinpoint precision in data interpretation and text output. - 🚫 Zero Hallucinations: Ensuring AI-generated content is always grounded in facts, preventing misleading or incorrect information. - 🔒 Built-in Data Privacy: Developing robust methods to protect sensitive information, a non-negotiable standard applicable to all data-rich sectors. - 💡 Probabilistic Confidence: Providing clear indicators of AI's certainty, enabling better decision-making and risk management. Applied Today: Expanding Impact Across Industries The advanced text generation and data handling capabilities perfected in healthcare are now directly enhancing operational excellence and strategic decision-making in diverse sectors: ⚖️ Legal Industry: Automating review of vast documents, ensuring precedent identification is accurate, and drafting summaries that meet strict legal standards. 📡 Telecom Industry: Powering precise network diagnostics, streamlining customer service responses, and predicting maintenance needs with data-driven text analysis. 💰 Finance Industry: Fortifying fraud detection, generating precise risk assessments, and ensuring compliance reporting is accurate and robust. This isn't a historical anecdote; it's a blueprint for deploying AI that you can unequivocally rely on, today. Leverage the battle-tested precision from industries where trust is paramount to unlock new levels of efficiency and insight in your own field. How is your industry prioritizing AI accuracy and trustworthiness? Share your insights! #AIforBusiness #TrustworthyAI #AccurateAI #DataPrivacy #CrossIndustryInnovation #HealthcareAI #LegalTech #FinTech #TelecomAI #AIApplications #BusinessSolutions

🏛️ Why Regulated Industries Are the Key to Unlocking General AI When people think of AI breakthroughs, they often look to open, high-scale domains like e-commerce or social media. But the real crucible for AI innovation has always been in regulated industries—where the stakes are highest, the rules are strictest, and the tolerance for error is close to zero. At Inference Analytics AI, we know this firsthand. Our journey proves that regulated AI is the foundation for general AI. ⚕️ Healthcare: Where It All Began Our first AI models were built in clinical contexts—where accuracy, explainability, and compliance weren’t “nice to haves,” they were life-or-death requirements. Every generated note or classification had to withstand clinical scrutiny. Every output had to align with privacy regulations like HIPAA. Every system had to be trusted by professionals who make high-stakes decisions. This environment demanded rigor, transparency, and reliability—values we carry into every model we build today. 📜 Why Regulation Forces Better AI Regulated industries like healthcare, finance, and insurance aren’t just “harder” markets for AI. They are the training grounds for trust. Regulatory requirements force clarity on data privacy, model explainability, and audit trails. Sensitivity to errors pushes us to design models that are robust, reliable, and bias-aware. Cultural care—working with professionals whose work impacts lives and livelihoods—teaches us to build AI that complements, not replaces, human expertise. These constraints don’t slow down innovation. They sharpen it. 💡 From Clinical AI to General AI — Powered by AI Studio What started in healthcare has evolved into solutions far beyond it. Text generation in clinical contexts taught us how to make language models precise, safe, and context-aware. Those same principles now power solutions in finance (risk, fraud detection, regulatory reporting) and contractual workflows (contract review, compliance checks, negotiation support). The key enabler? AI Studio, our no-code platform. It bakes in the rigor of regulated AI—security, explainability, and compliance—while giving enterprises the flexibility to build general AI workflows at scale. With AI Studio, organizations can: Spin up classification + GenAI pipelines without code. Apply secure enclaves and federated learning out-of-the-box. Ensure compliance and auditability in every workflow. 🌍 The Broader Implication The lesson is clear: 👉 AI that passes the test in regulated industries is AI that can succeed anywhere. The same care that lets a doctor trust a model in a hospital is what lets a CFO trust a model in a boardroom. Or a telco in their regulated contract compliance applications. 🚀 Our Point of View At Inference Analytics AI, we believe the path to general AI acceptance runs straight through regulated industries. 💡 Curious how lessons from regulated AI can apply to your industry? Let’s talk.

🚫 No Shortcuts in AI: The Hard Truth About Breakthroughs Every major technology revolution is a mix of excitement, speculation—and a hard truth: most experiments fail before the breakthrough happens. A recent MIT study found that nearly 95% of AI pilots fail. For many, that statistic is a warning. For us at Inference Analytics AI, it’s a reminder that true AI innovation isn't easy—and it's not supposed to be. 🛠️ Our Non-Linear Journey We know this firsthand. Our AI journey didn’t begin with today’s Large Language Models (LLMs). It began back in 2016, over time we built our own text narrative cloud—using models we developed in-house, long before GPTs were a household name. Our first use cases were in clinical workflows, where the stakes were high and the challenges even higher. Over the past six years, we’ve had to pivot use cases multiple times, learning what worked and what didn't. Each "pilot" was a critical step in a longer journey. Today, we’re proud to be live with one of our most significant AI implementations supporting thousands of users. The path was not linear, it wasn’t easy, and it certainly wasn’t hype. ⚖️ Why AI is Hard (and Different) So, why do so many AI projects fail? The reason is that AI isn't just software. It requires a completely different mindset. - It can change work itself: Reducing manual labor and reshaping job roles. - It’s stochastic: The same inputs can produce different outputs. - It needs constant tuning: Hallucination prevention, model refinement, and ensuring reliability are ongoing tasks, not a one-time setup. This isn’t about installing an app or running a script. It’s about building, improving, tailoring, and adjusting until the system consistently produces undeniable value. 📊 The Hard Truth & the Big Payoff Yes, there’s hype. Yes, some jobs will be reshaped. And yes, you will get it wrong—a lot. But here’s the bottom line: several of those 95% of "failed pilots" are just the first attempts. It takes multiple iterations to get it right. And when you finally do, the payoff is immense: the system becomes an indispensable part of your operations. 🚀 A Final Thought There are no shortcuts in AI. The winners won’t be those who got it right the first time. They’ll be the ones who kept building, learning, and pivoting until the value became undeniable. That’s the path we’ve taken at Inference Analytics AI—and it’s why we believe the real transformation is only just beginning. #AI #GenerativeAI #DigitalTransformation #Innovation #Technology #FutureOfWork #InferenceAnalyticsAI #NoShortcutsInAI

Model upgrades are change-management, not just model cards. GPT-5 saga = bugs, tone shifts, and user whiplash. The lessons hold for every AI product: - Pin & freeze. Let users keep last-known-good; don’t switch mid-session. - Canary > cliff. Ship behind flags, measure real tasks, then widen. - Run online evals. Regression gates on your data/workflows. - Keep fallbacks hot. One-click rollback + auto-fallback on regressions. - Protect voice. Offer a “compat” tone pack; personality changes matter. - Communicate like SRE. Pre-announce, status, and RCA. Where we stand Inference Analytics AI We deploy models in enterprise/PHI-sensitive healthcare environments with pinned versions, retrieval-first grounding, continuous evals, and one-click rollback. Trust beats novelty. https://lnkd.in/gn-Fr2gU P.S. We published a 12-point Healthcare Model Evaluation & Safety Rubric—happy to share. #AI #HealthcareAI #ProductManagement #LLMs #MLOps #DataGovernance #HIPAA #FHIR#AILaunch#AIInnovation#GenAI #LLMs #MachineLearning #Tech #Innovation #Data

Conversations in Doha: AI, Healthcare, and New Friendships On my way back to the U.S., after visiting family and the Inference Analytics AI offshore teams, I took a short break in Doha, Qatar, and had the opportunity to meet with Dr. Dr. Fatih Mehmet Gul, CEO of The View Hospital an affiliate of Cedars-Sinai. Having previously served together as AI advisors to Abbott, it was great to reconnect and dive into how healthcare is evolving across the Gulf. What struck me most is Dr. Fatih’s vision for advancing care globally and in particular in regions like Qatar, Saudi Arabia, and Dubai. He is truly one of the leaders shaping healthcare, combining deep clinical expertise with a forward-looking approach to technology, AI and innovation. We talked about how AI and technology can make care smarter and more personalized—helping doctors, improving patient outcomes, and enhancing care delivery without adding complexity. I left our conversation energized about the possibilities for AI to support smarter, more personalized care—and grateful for the chance to turn professional connections into real friendships. #AI #ArtificialIntelligence #HealthcareInnovation #DigitalHealth #HealthTech #Leadership #GulfRegion #MiddleEastHealthcare #Qatar #SaudiArabia #Dubai #FutureOfHealthcare #MedTech #InferenceAnalytics #theviewhospital

🤖 Why AI Is Challenging for Companies to Absorb Recenly, I’ve written about why AI is not just software. It’s technology—but not like the tech most organizations are used to managing. Traditional IT systems were designed as support infrastructure. Their purpose? Automate documentation, streamline access, secure data, generate reports. They power the back office, where rules are known and behavior is consistent. But AI doesn’t play by those rules. And this makes it uniquely difficult to absorb. 🌀 AI Lives in Both Worlds—And That’s Disruptive At Inference Analytics AI, we’ve worked across industries—from healthcare to manufacturing to mobility—and we see this tension everywhere. Unlike ERPs or CRMs, AI shows up in the front office, not just the server room: - A physician facing Agentic assistant - A sales enablement tool embedded in CRM workflows - A contract review model trained to replace junior analysts - A lane-detection system on edge silicon that thinks in real time This isn’t IT. It’s behavior change. And that’s why the challenge of implementation goes far beyond technology. 🧠 Why the Old IT Playbook Doesn’t Work When CEOs and boards get excited about AI, the natural instinct is to “route it to IT.” But that mindset assumes AI is something you “deploy.” Like a database. Or a firewall. Here’s what we’ve seen at InferenceAnalytics: - AI is not a stack. It’s an evolving capability. - It’s not software. It’s a judgment layer. - It doesn’t live in the back office. It blurs roles, replaces playbooks, and reinvents decision-making. The people who drive AI forward in real organizations are not traditional IT managers. They’re hybrid thinkers—those who understand both domain-specific nuance and model behavior. They understand the difference between a feature and a function—but also between an insight and a hallucination. 🏗️ The New AI Operating Model At Inference Analytics AI, we’ve stopped treating AI as “just another transformation.” Instead, we help companies: - Redesign workflows for AI-native speed and iteration - Equip non-technical stakeholders with prompt literacy and grounding logic - Embed feedback, observability, and outcome metrics into every loop AI transformation isn’t linear. It’s not a rollout. It’s an evolution. And it doesn’t fit into your legacy org chart. It reshapes it. 💡 Final Thought AI is hard to absorb—because it asks companies to change faster than they’re used to. But that’s exactly where the value lies. At Inference Analytics AI, we’re not just building models. We’re helping companies reimagine how work gets done, where decisions live, and how intelligence flows through systems. It’s not IT. It’s not support. It’s a new layer of enterprise capability—and the companies who adapt will outpace those who overthink. #AITransformation #EnterpriseAI #GenAI #AIImplementation #InferenceAnalyticsAI #OrganizationalChange #FrontOfficeAI #LLMs #RealWorldAI #Leadership #FutureOfWork