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, 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.

Fashion imagery is entering a new phase, one where AI doesn’t just edit photos, it creates them. Graswald AI, founded by Julius Harling, builds a digital photo studio that lets brands generate lifelike product images and campaign visuals without physical shoots. Using proprietary generative models trained on synthetic data, Graswald can dress virtual avatars in real garments with pixel-accurate fidelity to fabric, texture, and fit.

In this episode, Julius explains how their multi-model system works: from training AI on structured garment data to creating consistent lighting and brand-specific aesthetics. We discuss how digital avatars are designed with real emotional expression, why brands are demanding exclusive AI models that reflect their casting choices, and how synthetic data pipelines enable scale without copyright risks.

The conversation also unpacks the economics behind this shift: Why photo production costs can drop by more than 90%, how AI lets creative teams test ideas that were previously too expensive to shoot, and what this means for supply chains, sustainability, and time-to-market.

We close on the bigger picture: the convergence of multimodal foundation models, falling inference costs, and the rise of context-aware AI systems that will make content generation as fluid as sketching an idea.

If the past decade digitized retail, this one is about digitizing creation itself.

AI is changing how data moves. In this episode, we sit down with Matthaus Krzykowski, co-founder and CEO of dltHub, the company turning data pipelines into a native language for large language models. dltHub automates up to 95 percent of data-engineering workflows and is already used by thousands of developers and over 4,000 companies. Its open-source roots and recent selection for the OpenAI Startup Program reveal how quickly AI infrastructure is evolving.

Matthaus walks us through the rise of vibe coding, the launch of dltHub’s marketplace with more than a thousand AI-first code connectors, and what happens when agent workflows start running in production. We also unpack how MCP brings natural-language interaction to company data, why Python remains the connective tissue of AI systems, and how auditability and data quality are becoming the real frontier for automation.

A conversation about infrastructure catching up with intelligence—and the new rules of building in an autonomous world.