Alexander Oelling (Founder & CEO, INXM) on Building Deterministic AI for Enterprise Processes Reliably

Alexander Oelling (Founder & CEO, INXM) on Building Deterministic AI for Enterprise Processes Reliably

The prevailing narrative in AI suggests that spending more tokens guarantees more intelligence and reliability. But what if the key to dependable enterprise AI is actually spending fewer, or even zero, tokens on repeated tasks? In this episode, we speak with Alexander Oelling, founder and CEO of INXM, a company building a reliable execution layer for complex business processes. Alexander shares how INXM’s "Compiled AI" solves the core hurdles of enterprise LLM adoption: hallucinations, high token costs, and a lack of repeatability. He explains how their system initially uses advanced AI agents to learn and master a workflow—such as processing complex SAP invoices—and then compiles those success vectors into a simple, deterministic executable artifact. This approach makes routine enterprise process execution auditable, mathematically correct, and up to 100 times cheaper than relying on continuous model inference. The conversation then expands into the broader vision of the "autonomous company" and the rise of "invisible AI". Alexander discusses how human roles will shift toward outcome-based work, where employees are valued for asking the right questions and controlling results while AI seamlessly executes tasks in the background. We also explore why the next major leap in AI might not come from massive cloud models, but from highly efficient, localized systems interfacing directly with legacy enterprise software to eventually create digital twins of entire organizations. If you’re interested in how compiled AI is overcoming the limits of current LLMs, bringing deterministic reliability to the enterprise, and laying the groundwork for autonomous organizations: this episode is worth a listen.

#7 Ingmar Klein (CEO, Huzzle) on the Future of Work, AI, Hiring, and Human Data.

AI may not just replace work. It may also create entirely new categories of human work around training, evaluating, and operating intelligent systems. In this episode, we speak with Ingmar Klein, co-founder and CEO of Huzzle, a company that evolved from a talent marketplace into an AI-powered recruitment engine and, more recently, a provider of human data for frontier AI labs. Ingmar shares how Huzzle built AI interview systems capable of assessing candidates at scale, why hiring is one of the first workflows where AI can already outperform humans in consistency and efficiency, and what it takes to combine automation with human judgment in recruitment. The conversation then expands into the emerging market for human feedback and training data. Ingmar explains why experts across domains may increasingly spend time evaluating model outputs, improving agents, and helping AI systems operate inside real software environments. We also discuss why the next bottleneck may not be model capability, but adoption inside companies still running on legacy systems. If you’re interested in how AI is changing hiring, creating new job categories, and reshaping how organizations operate: this episode is worth a listen.

#6 Building the Voice AI Category: Daniel Keinrath (CEO, Fonio AI) on Product, GTM, and Scale

Voice AI is becoming one of the first real entry points for AI adoption in SMEs: not because of the technology itself, but because it delivers immediate, measurable value. In this episode, we speak with Daniel Keinrath, co-founder and CEO of Fonio AI [https://www.fonio.ai/en], an AI call assistant used by thousands of businesses across the DACH region. Daniel shares how Fonio AI grew from 0 to rapid scale, why voice is a natural starting point for AI in non-technical companies, and how this expands into a broader omni-channel platform over time. We discuss the practical challenges behind building in this space: getting companies to structure their data, integrating with fragmented systems, and making AI usable for non-technical users. The conversation goes beyond product into market dynamics. Daniel explains why this is a scale-driven market with similarities to telecom, where distribution, partnerships, and speed matter as much as the underlying technology. We also cover why Fonio focuses on SMEs instead of enterprise, and why long-term success depends less on building proprietary models and more on product, go-to-market, and execution. If you’re interested in how AI is actually being deployed in everyday business workflows, and what it takes to build a category in a fast-moving market: this episode is worth a listen.

#5 AI-Written Software: Tyler Dunn (Co-Founder, Continue) on the Future of Coding

In this episode of Cutting Edge AI, Jens Lapinski and Robin Harbort speak with Tyler Dunn, Co-Founder of Continue [https://www.continue.dev/], about how AI is transforming software development. As AI agents increasingly write code, a new question emerges: who reviews it? Tyler explains why the future of software may involve AI reviewing AI, and why developers may soon stop reading most of the code they ship. The conversation explores the rise of continuous AI, the need for a mission control layer to govern fleets of coding agents, and how trust, testing, and automation will replace traditional code review. They also discuss the future of programming languages, agentic development workflows, and why the next generation of developers may think less in code and more in systems. Topics include: * AI agents writing and reviewing code * Continuous AI and automated agent checks * Mission control for fleets of developer agents * The future of programming languages * Why developers may stop reading code entirely

#4 From Legacy Systems to Agentic AI: Fabian Heinrich (CEO, Mercanis) on the Future of Procurement

Procurement is one of the largest and most complex parts of the global economy – over $100 trillion a year – yet much of it still runs on spreadsheets, email threads, and legacy systems built decades ago. Mercanis [https://www.mercanis.com/], founded by Fabian Heinrich, is taking a different approach: rebuilding procurement from the ground up around agentic AI. In this episode, Fabian explains why procurement was historically underserved by modern software, how his first startup exposed the inefficiencies behind RFPs, supplier comparisons, and contract negotiations, and why large enterprises still rely on Excel despite the stakes involved. We unpack what “agentic procurement” actually means in practice, from AI agents reading hundreds of pages of contracts to running complex pricing scenarios and orchestrating sourcing workflows end to end. We also discuss the recent inflection point in enterprise demand for AI: procurement teams suddenly have budget, and board-level interest in automation has changed buying behavior, and why heavy industries like automotive, chemicals, energy, and pharma are now at the center of AI-driven transformation. The conversation goes deeper into the future of work inside procurement: how the role of the buyer is shifting toward orchestration and analysis, what human-in-the-loop really looks like when agents negotiate with other agents, and how procurement systems will increasingly sit on top of, and eventually eat into, legacy ERP and compliance infrastructure. If the last decade optimized procurement interfaces, the next one is about autonomous systems running the process itself.