Brands are losing visibility because they rely on click-based attribution in a world moving toward answer engines. Daniel Kazani speaks with Malte Landwehr, CPO and CMO of Peec AI, about the reality of AI search. Malte explains why 25% of leads might come from LLMs even when tracking software shows 0%. They discuss the transition from SEO to GEO, why static dashboards are disappearing, and the hard truth that average marketing work is becoming valueless.

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Guest Bio

Malte Landwehr is the Chief Product Officer and Chief Marketing Officer at Peec AI. He has over 20 years of industry experience, including roles as VP of SEO at idealo and VP of Product at Searchmetrics. Peec AI helps marketing teams understand and optimize their visibility in LLM-based search engines.

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What We Cover

1. Why click-based tracking fails to capture the user journey inside ChatGPT and Perplexity.
2. The specific role of "grounding sources" such as Reddit, YouTube, and LinkedIn in AI responses.
3. How Peec AI simulates user behavior to track brand mentions and sentiment.
4. The shift from static data dashboards to on-demand AI chat interfaces.
5. Why is average marketing becoming free while top-tier marketers become exponentially more effective?
6. The challenge of finding product managers with strong product taste in Europe.

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Resources Mentioned

1. Peec AI
2. Malte Landwehr (LinkedIn)
3. Daniel Kazani (LinkedIn)
4. ChatGPT
5. Perplexity
6. Google Gemini
7. Granola
8. HubSpot

Learn more about facial vital sign detection: shen.ai & caire.ai

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Healthcare has historically lagged in digitalization, creating a significant opportunity for artificial intelligence to jump-start the industry. Host Daniel Kazani sits down with Dr. Lucas Mittelmeier, an investor at Heal Capital, to discuss why the sector's heavy administrative burden makes it a prime target for disruption. They explore the reality of "Shadow AI," where physicians bypass slow hospital IT systems to use tools like ChatGPT for daily tasks. Lucas explains how the industry is splitting into two distinct speeds: highly regulated clinical tools and agile administrative workflows. The conversation also highlights cutting-edge innovations, including facial analysis software that reads vital signs via a camera and vocal biomarkers that detect heart failure.

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Guest Bio

Dr. Lucas Mittelmeier is a physician-turned-investor at Heal Capital, a leading European healthtech venture capital firm. With a background bridging clinical medicine, strategy consulting, and startup leadership, he evaluates companies through both medical and business lenses. He is also the author of the Healthtech Off The Record newsletter, where he provides data-driven analysis of industry trends. At Heal Capital, he focuses on sourcing and leading deals from Pre-Seed to Series A.

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What We Cover

1. Why the lack of legacy digital infrastructure in healthcare might actually accelerate AI adoption.
2. The phenomenon of "Shadow AI" and why doctors are using consumer tools despite strict hospital regulations.
3. How administrative AI is moving faster than clinical diagnostic tools due to lower regulatory barriers.
4. The potential for "facial parameters" in which video can detect heart rate, blood pressure, and oxygen saturation.
5. Using vocal biomarkers to identify conditions like heart failure by analyzing fluid buildup in the lungs.
6. How typing patterns on a keyboard can serve as early indicators for depression.
7. Why specialized "AI Therapist" startups have struggled to compete with general Large Language Models.
8. The four key moats for healthtech startups: data advantages, network effects, deep customer service, and brand trust.

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Resources Mentioned

1. Heal Capital
2. OpenAI (ChatGPT)
3. Anthropic...

AI Trends in Germany - Presentation (PDF) — Follow along with the data discussed in this episode

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Germany currently faces a distinct tension between its technical potential and actual financial commitment to artificial intelligence. While the country ranks high in AI skills and research, private investment stands at just 1.8 billion euros, compared to over 62 billion in the United States. Host Daniel Kazani sits down with Stephan Fricke to examine the reality behind these numbers and what they mean for the German market.

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Stephan breaks down the data on Germany's current 45,000 AI specialists and the projected gap of nearly 180,000 by 2032. They discuss why customer contact centers are seeing 88% of implementations and how manufacturing giants like BMW and Siemens are using AI for practical quality assurance. The conversation also covers the critical role of strategic partnerships and outsourcing in bridging the talent shortage that domestic training alone cannot solve.

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👤 Guest Bio

Stephan Fricke is the CEO of the Deutscher Outsourcing Verband e.V. (German Outsourcing Association) and the Deutscher Process Automation Verband. Since 2010, he has focused on bridging the gap between German business culture and global innovation hubs. Through industry publications such as the Outsourcing Journal, Stephan shapes the narrative around Global Business Services and advocates for diversifying sourcing destinations to address the talent crisis in the DACH region.

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📌 What We Cover

1. The estimated 60 billion euro market volume for AI services in Germany in 2025.
2. Why 88% of German companies implementing AI start with customer contact and chatbots.
3. The massive gap in private AI investment: 1.8 billion euros in Germany versus 62.5 billion in the US.
4. How Germany compares globally in terms of infrastructure, with a notable lack of data centers.
5. The talent crisis: Moving from 45,000 specialists today to a need for 180,000 by 2032.
6. Why Softup and similar partners are becoming essential for companies unable to find local talent.
7. Specific manufacturing use cases for AI: From predictive maintenance at Siemens to quality assurance at BMW.
8. The regulatory hurdles and slow government strategies are affecting European competitiveness.

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🔗 Resources Mentioned

1. Deutscher Outsourcing Verband e.V. (German Outsourcing Association)

Manufacturing is no longer just about moving atoms. It is shifting toward software-defined automation and fully autonomous systems. Daniel Kazani sits down with Miroslav Kriz, Principal Partner at Momenta, to discuss how AI is reshaping the factory floor. They explore why industrial innovation requires different safety standards than typical software, where a "hallucination" can mean physical danger rather than just bad code.

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Miroslav explains the reality of "lights out" factories, where blast furnaces adjust in real time without human input. He also critiques the "tourist syndrome" that European founders face when entering the US market and argues why industrial startups should look to Pittsburgh or Indianapolis rather than Silicon Valley. This conversation covers the journey from simple automation to true autonomy and the specific physics that investors look for before writing a check.

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Guest Bio

Miroslav Kriz is a Principal Partner at Momenta, a venture capital firm focused on industrial impact and enterprise technology. He specializes in bridging the gap between legacy industrial companies and modern innovation.

Currently based in Prague after moving from New York, Miroslav works to connect Central and Eastern European technical talent with the US market. He also helps lead initiatives like Gem7 to help startups establish operational beachheads in America.

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What We Cover

1. The three core pillars of industrial impact are software-defined automation, robotics, and AI.
2. Why the "move fast and break things" mentality fails in manufacturing, where safety is critical.
3. How virtualization allows agile development on machines with 30-year lifecycles.
4. The emergence of "lights out" factories and autonomous closed-loop systems.
5. Why ROI in industry is defined by speed and waste reduction rather than quality improvements.
6. The "tourist" mistake European founders make when expanding to the US.
7. Why industrial startups often find better success in Detroit or Milwaukee than in Silicon Valley.
8. Using AI in venture capital to validate physics and research trends rather than make deal decisions.

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Resources Mentioned

1. Momenta
2. Gem7 (Market entry service)
3. Rockwell Automation
4. Fleet Space
5. Grok

This episode was recorded on Dec 10, 2025.

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Automation and digitalization were huge topics for decades, but “it’s no longer enough.” Host Daniel Kazani talks with Pascal Faerber, Managing Director, Digital Services Germany at Orange Business, about agentic AI and why it is “fundamentally different” from reactive gen AI. Pascal frames agentic AI as proactive, understanding goals and desired outcomes, breaking them down into steps, executing across multiple systems, evaluating its own output, and learning continuously.

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The conversation moves from digital transformation and cloud, including hyperscalers like Azure and Amazon, to a concrete example: a customer success AI agent that scans incoming customer messages across channels, classifies issues, prioritizes urgency, fetches relevant internal knowledge, drafts proposed solutions, triggers actions across systems, and escalates only when human judgment is required. They also talk about AI as a transformation: leadership mindset, processes, and foundations that enable a network of collaborating humans and agents.

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Pascal Faerber is Managing Director, Digital Services Germany at Orange Business. He describes Digital Services as “top of the spear” in digital transformation, supporting clients with cloud transformation, data and AI, data platform development, and AI use case development. Pascal also mentions being a lecturer and a business angel, as well as being very active in the tech community.

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1. Orange Business Digital Services and “digital transformation,” including cloud transformation and working with hyperscalers like Azure and Amazon
2. A sovereign cloud solution with regulatory requirements and environments operated in Europe by European employees
3. “Agentic AI” as proactive systems that understand goals, breaks them into steps, executes across multiple systems, evaluates output, and learn continuously
4. A customer success AI agent: scanning multichannel messages, classifying and prioritizing issues, pulling contracts, SLAs, documentation, and ticket history, then triggering actions across systems
5. Impact discussed: resolution time brought down from “two, three days” to “15, 30 minutes,” and “60, 65, 70%” reduction in repetitive work
6. AI adoption as a transformation, “mindset first,” and the bottleneck being “permission” rather than technology
7. Clearly defined roles in human and AI collaboration, and AI as “a new colleague.”
8. Moving from pilots to scale: five questions, one high-impact breakthrough, and scaling aggressively with training and change

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AI has shifted from a buzzword to a genuine, intelligent assistant. Host Daniel Kazani talks with Dajana Stojchevska, a senior software engineer at Google in Munich, about how AI is embedded in day-to-day engineering work, not about replacing engineers, but about boosting productivity and removing friction so teams can focus on the cool stuff.

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Creation, collaboration, and knowledge management show up everywhere, from intelligent code completion inside the integrated development environments, to AI-assisted code reviews, to meeting notes that summarize transcripts, highlight key decisions, and list action items with owners. The conversation also remains grounded in the challenges: the hallucination trap, prompt injection, indirect injection hidden in external data such as a PDF, and strict discipline around data privacy. Looking ahead, the focus turns to autonomous agents, massive context windows, proactive analysis, and the evolving role of the software engineer as architect and orchestrator.

Dajana Stojchevska is a senior software engineer at Google in Munich. She graduated with a degree in Scopia from the Faculty of Computer Science and Engineering, with elective subjects in software engineering. She completed a few internships, including in Python, and her first role was as a Java developer focused on full-stack web development with Java and Angular. She also worked as a laboratory teaching assistant, helping students with exercises. After about two years, she moved to Germany for the Google offer.

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1. AI inside the editor as a proactive teammate, intelligent code completion, prompts for snippets, and boilerplate
2. Code reviews with AI, drafting descriptions, style and standards fixes, and automated fixes from static analysis errors
3. AI-generated suggested code edits from teammate feedback, plus reviewer support with links to documentation
4. Meeting notes that summarize transcripts, highlight key decisions, and list action items with owners
5. Generating architecture diagrams from text, plus document analysis and “interviewing the document”
6. Correctness and the hallucination trap, treating AI like a junior engineer who needs supervision
7. Security risks, direct prompt injection, indirect injection, and why even a PDF can be a hostile input
8. Data privacy and strict guidelines on what data can go into which tools, plus internal AI chatbot support
9. Staying up to date with internal channels, newsletters, tech talks, hands-on daily practice, and peer community
10. The next five years: autonomous agents, bigger context windows, proactive help, and...

Being discoverable on ChatGPT has become a board-level problem for most companies. Host Daniel Kazani talks with Vlad Shvets about Qvery, an AI agent software that helps brands measure and grow their visibility and share of voice on the AI search engines. Measuring brand visibility is a new challenge companies need to address, requiring new tools and software. The way we search Google is very different from how we use ChatGPT, which provides comprehensive recommendations personalized to your conversation history, language, and location. The quest starts when a CEO goes to chat, asks a question related to their brand, and the brand does not appear. The marketing team begins assessing how to measure this and how to make the brand discoverable. Vlad breaks down three pillars: your own website presence, mentions on external websites, and user-generated content, and why Reddit is the most critical website these days.

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Vlad Shvets is a marketing expert, serial entrepreneur, advisor, and founder of Qvery. Qvery is an AI agent software that helps brands measure and grow their visibility and share of voice on the AI search engines. Vlad shares that Qvery started as a consultancy, then evolved into a way to measure visibility on chat GT, including personalized results. He describes a vision of the future of the web as agentic, with people increasingly relying on AI agents to do tasks for them.

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1. When a CEO goes to ChatGPT, asks a question related to their brand, and the brand does not pop up
2. Google gives you links, and ChatGPT gives you complete recommendations, with personalization
3. A case study for services, a separate domain, a single-page website, and leads from ChatGPT and Google AI overviews
4. Optimizing for specific granular use cases, capturing high-intent requests, and vanity metrics
5. Three pillars: your own website presence, mentions on external websites, and user-generated content, Reddit in particular
6. FAQ schema and schema data as fast food for chat gt to fetch and understand
7. Logged in state of personas, citations list, outreach, and getting a product mentioned where it matters
8. CloudFlare blocking agents, browser manipulation tech, AI agent regulation, and a passport program
9. Google AI mode is becoming a default way to search, and it's what happens overnight for companies and businesses

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1. ChatGPT
2. Google AI mode
3. Gemini 3 model
4. OpenAI

We are living through the most significant platform shift since the Internet. Host Daniel Kazani talks with guest Shefqet Avdullau, an angel investor, advisor, and speaker focused on growth-stage B2B SaaS, FinTech, ad tech, and health tech. The conversation starts with a story that moves from coding to multiple ventures to a meaningful exit, then into investing with a mentor who gave a head start on due diligence, pitfalls, and strategies. The weight falls on the team because the idea you start with does not necessarily mean you will end with it, and a good team can turn a bad idea into a great one. Then: AI and defensibility, wrappers, data-loop strategy, fine-tuning, and what happens if OpenAI or Gemini releases a new update tomorrow. Health tech and biotech, drug discovery, and turning biology into an engineering problem.

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Shefqet Avdullau is an angel investor, advisor, and speaker. He invests in serial founders across the US and UK, focusing on B2B SaaS, FinTech, and ad tech at all stages, and on health tech specifically at the growth stage. His foundation is in tech; he worked in that field for about 13 years, started multiple ventures with some small exits, and then had one meaningful exit. In about four years, he has done about 16 investments and has had two exits.

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• From coding, to multiple ventures, to a meaningful exit, to investing and joining a group of investors
• A mentor with private equity experience, due diligence, pitfalls in investing, and strategies to follow
• Why serial founders come with a map, with a playbook, and go straight to finding product market fit
• Scars, lessons, when things get tough, and why failure can be something you prefer
• Idea versus team, pivots, and why the team can turn a bad idea into a great idea
• Two founders or more, complementary skillset, product, and sales, and a third on operations
• Founder problem fit, domain experience, network, and solving an actual problem, not just for money
• AI wrappers versus defensibility, data loop strategy, fine-tuning, and “would this company die” after a new update
• Where AI is disrupting, health tech and biotech, drug discovery, simulating millions of interactions digitally, and FinTech underwriting with unstructured data
• Using AI for competitor analysis, risk analysis, and alternative potential revenue streams, and “it hallucinates a lot”
• A contrarian investment choice, two serial founders, employee disengagement, productivity, and invisible frictions

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• Open AI
• Gemini
• Figma
• Nvidia
• LinkedIn
• lovable