Join us for an insightful conversation with Ekrem Namazci, founder of LIYA and former Data and AI Lead at Microsoft. Ekrem shares his journey from an unconventional background to tech leadership, and explains his pioneering approach to building skills in the AI era. Learn why he believes skills are increasingly more valuable than degrees, and how his "STAR" methodology (Skills Through Alternative Routes) can help anyone design their career for future success.

TOPICS DISCUSSED:

1. Evolution of Technology Access
Ekrem explains how technology has evolved from being created by experts for experts, to being created by experts for everyone, to now where anyone can create and use technology with AI tools.

2. Skills vs. Degrees
Why university degrees alone are becoming outdated before graduation, and how combining skills with practical experience creates a stronger career foundation.

3. The STAR Method
Skills Through Alternative Routes - how to gain and prove valuable skills through non-traditional paths like bootcamps, projects, and mentorship.

4. Mentorship Benefits
The power of "learning on the shoulders of giants" through mentorship, and how even just two sessions can provide essential career guidance.

5. Future of Recruiting
How skill-based hiring is transforming industries beyond tech, focusing on demonstrated abilities rather than formal qualifications.

6. Building Evidence of Skills
Techniques for proving your skills through storytelling, STAR framework (Situation, Task, Action, Result), and personal branding.

7. Managing Multiple Passions
Ekrem's approach to balancing multiple professional interests and scaling LIYA across different markets.

INSIGHTS:

- Technology evolution has created a third wave where AI tools allow anyone to develop and use technology
- Domain knowledge is increasingly valuable as AI handles more technical tasks
- Degrees remain valuable but must be supplemented with practical skills
- Mentorship can provide crucial guidance with as little as two sessions
- Storytelling about your skills is more powerful than listing them on a resume
- Public content creation (LinkedIn posts, etc.) helps document and validate skills
- Focus on one skill for 10 minutes daily to build expertise over time

TOOLS AND TECHNOLOGIES MENTIONED:

- LIYA (AI-powered skill-building platform)- LinkedIn (for networking and personal branding)- ChatGPT, AI agents, no-code tools- Computer vision technologies

CONTACT INFO:

- LinkedIn: Ekrem Namazci- LIYA on App Store: Download- LIYA on Google Play Store: Download

CHAPTERS
00:00 Introduction and Background01:46 Evolution of Technology Access04:53 Skills vs. Degrees07:44 Designing Your Career Path10:31 The Power of Mentorship13:26 The Future of Recruiting17:42 Proving Your Skills24:09 Personal Branding Through Writing25:56 Managing Multiple Passions31:12 Surviving in the AI Era34:58 How to Connect with Ekrem and LIYA

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Join us for a special International Women's Day episode of AI Ketchup where we explore the intersection of AI and FamilyTech with Rosaria Di Donna, founder of familymind.ai. After working on Microsoft's $1 billion Microsoft 365 business, Rosaria made a bold pivot to tackle the $2 trillion unpaid family care market with technology designed to ease the burden on working parents.

TOPICS DISCUSSED:

1. From Corporate Success to Purpose-Driven Entrepreneurship
Rosaria shares her journey from leading Microsoft's modern work and security ecosystem to founding familymind.ai, driven by a deep sense of purpose to help families manage their overwhelming responsibilities.

2. The FamilyTech Revolution
Learn how familymind.ai is creating an AI-powered ecosystem with multiple specialized AI agents designed to tackle the 85% of challenges that most families share, while reducing the mental load that disproportionately affects women.

3. Beyond Technology: A Holistic Approach
Discover how familymind.ai is building more than just an app - they're creating an ecosystem with 35+ partners including coaches and nonprofit organizations to address family challenges from multiple angles.

4. Ethical AI and Cultural Considerations
Rosaria discusses the importance of developing ethical AI that doesn't simply reinforce historical biases, especially when addressing the complex cultural differences in family dynamics.

5. Work-Life Integration for Working Parents
Explore how the right technology can give families more quality time together by automating and streamlining family management tasks.

INSIGHTS:

- One out of three women with children under six experiences burnout
- 58 billion hours of unpaid work falls primarily on women's shoulders
- 85% of family struggles are universal despite cultural differences
- Children of parents with mental health issues are five times more likely to experience similar issues
- Technology should give families time back for what truly matters: connection

TOOLS AND TECHNOLOGIES MENTIONED:

- Familymind.ai multi-AI agent system
- AI-powered family planner
- AI family chatbot fueled by family data
- Microsoft Copilot (as inspiration)

CONTACT INFO:

- LinkedIn: Rosaria Di Donna
- Website: familymind.ai

CHAPTERS
00:00 familymind.ai and Rosaria's Journey

07:17 The Transition from Corporate to Family Tech

12:02 Embracing Risk and Purpose in Entrepreneurship

13:35 Developing Family Mind AI: Features and Approach

17:50 The Bigger Picture: Family Mind as a Solution

22:39 Identifying Red Flags in Family Dynamics

27:36 Cultural Perspectives on Family Dynamics

29:51 Defining Equality Across Cultures

31:37 The Role of AI in Family Management

34:52 Ethical AI and Family Dynamics

38:14 Targeting Working Parents for Family Support

40:10 Encouraging Family Time and Connection

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Join us for an insightful conversation with Amine Ait El Harraj, Practice Manager for Data & AIML at Reply, as we explore the world of Responsible AI implementation and EU AI Act compliance. Discover how companies can effectively balance innovation with regulation, and learn practical strategies for implementing AI systems that are ethical, fair, transparent, and accountable.

TOPICS DISCUSSED:

1. Responsible AI Dimensions
Amine breaks down the six key dimensions of Responsible AI: fairness and inclusiveness, reliability and safety, transparency, privacy and security, accountability, and controllability.

2. EU AI Act and Regulation
Learn about the history and purpose of the EU AI Act, how it impacts both AI developers and deployers, and why standardization benefits the industry.

3. Managing AI Vulnerabilities
Discover the primary vulnerabilities of AI systems including hallucinations, security risks, irrelevant content, and data leakage, and how to prioritize them based on your use case.

4. Cost Optimization for AI Workloads
Practical strategies for reducing the total cost of ownership for AI and data workloads, including mimicking production environments and smart data management.

5. Future-Proofing AI Systems
How multi-agent architectures and decoupled systems can help companies adapt to changing regulations and technological developments.

INSIGHTS:

- Responsible AI has become crucial with the rise of general-purpose AI systems like ChatGPT
- The EU AI Act places most responsibility on foundation model developers while providing a clear framework for deployers
- Standardization of AI systems across the EU market can actually boost innovation in the long run
- Testing AI systems through "red teaming" helps identify vulnerabilities before deployment
- Breaking AI systems into smaller agents can reduce the impact radius of regulation changes

TOOLS AND TECHNOLOGIES MENTIONED:

- ChatGPT and general-purpose AI models
- Mistral AI
- Amazon Q
- OWASP Top-10 for Large Language Models

CONTACT INFO:

- LinkedIn: Amine Ait El Harraj

CHAPTERS
00:00 Understanding the EU AI Act
05:25 Dimensions of Responsible AI
10:38 Challenges and Risks in AI Implementation
15:13 The Impact of Regulations on Innovation
21:34 Cost Optimization in AI Workflows
27:08 Future-Proofing AI Architectures

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In this episode, we dive into AI development strategies with Nico Finelli, Founding GTM at Vellum.ai. Drawing from his experience with autonomous vehicles at M City and his work at Weights & Biases, Nico shares valuable insights on building AI agents through an incremental, test-driven approach.

TOPICS DISCUSSED:

1. Incremental AI Development
Nico draws parallels between autonomous vehicle development and AI agent building, emphasizing the importance of starting with constrained tasks and gradually expanding capabilities. He illustrates how this approach can be applied to various domains, from legal to healthcare applications.

2. AI Implementation in Healthcare
Through case studies like DeepScribe, Nico demonstrates how AI can enhance healthcare workflows while maintaining human oversight. He discusses practical approaches to evaluation and implementation in sensitive domains.

3. State of AI Development
Drawing from Vellum's comprehensive developer survey, Nico shares insights about AI implementation challenges, highlighting that only 25% of teams successfully reach production, and discusses strategies for improving these outcomes.

4. Evaluation Frameworks
The conversation explores practical approaches to evaluating AI systems, emphasizing the importance of structured testing and feedback loops in development cycles.

INSIGHTS:

- The value of constraining AI problems to build competency gradually

- Why human oversight remains crucial in sensitive AI applications

- The importance of robust evaluation frameworks in AI development

- How implicit and explicit feedback shapes AI system improvement

- The role of domain experts in defining AI system constraints

TOOLS AND TECHNOLOGIES MENTIONED:

- Vellum SDK

- Electronic Health Records (EHR) Systems

- LLM Orchestration Platforms

- SOAP Notes

- Test-Driven Development Frameworks

USEFUL LINKS:

- State of AI Report 2025:

- Vellum Case Studies:

- Vellum SDK Documentation:

CONTACT INFO:

- LinkedIn:

- Email: [email protected]

CHAPTERS

00:00 Path to a Founding GTM at Vellum.ai

07:03 Insights from Weights and Biases and Vellum.ai

14:05 Building AI Agents: Lessons from Autonomous Vehicles

21:44 The State of AI: Insights from Developer Reports

28:02 Vellum SDK: Enhancing AI Development and Evaluation

34:34 Encouragement for Aspiring AI Builders

In this episode, we explore the intersection of AI security, vector databases, and career transformation with Thierry Damiba, Developer Advocate at Qdrant. From his experience securing sensitive government applications to pioneering vector database implementations, Thierry shares valuable insights on building secure AI systems and navigating technological change.

TOPICS DISCUSSED:

1. Vector Databases and AI Security
Thierry explains how vector databases have become the ideal data management tool for AI applications, discussing their role in securing sensitive data and implementing effective access controls. He shares practical approaches to preventing hallucinations and data leakage in AI systems.

2. Security in the Age of AI Agents
The conversation delves into the implications of AI agents for security, exploring both the challenges and opportunities they present. Thierry discusses how automation is actually increasing the value of deep technical understanding while making technology more accessible.

3. HNSW Algorithm and Vector Search
Through an engaging library analogy, Thierry breaks down the complexities of the HNSW algorithm, explaining how it enables efficient vector search at scale and why this matters for modern AI applications.

4. Career Evolution in the AI Era
The discussion examines the changing landscape of technical careers, with insights on adapting to automation and finding fulfillment in technological work. Thierry shares personal experiences of prioritizing passion over immediate financial gain.

INSIGHTS:

TOOLS AND TECHNOLOGIES MENTIONED:

CONTACT INFO:

CHAPTERS
00:00 The Rise of Vector Databases
01:54 Security in AI Applications
05:14 Guardrails for AI Systems
08:13 Jailbreaking and Input Validation
09:54 AI Agents: Opportunities and Risks
16:53 The Future of Work and Automation
25:45 GPU Indexing and Application Development

In this episode, we dive deep into the world of knowledge graphs and organizational change with Vadym Safronov, a Lead Data Scientist at Nielsen IQ and veteran of enterprise transformations. From building actual ketchup factories to architecting complex data systems, Vadym shares fascinating insights on how knowledge graphs can revolutionize enterprise operations and drive successful organizational change.

TOPICS DISCUSSED:

1. Knowledge Graphs Fundamentals
Vadym breaks down the concept of knowledge graphs through practical examples, explaining how simple subject-predicate-object relationships can be used to build complex knowledge systems. He illustrates how these structures can be enhanced with neural networks to predict patterns and relationships.

2. Enterprise Transformation
The discussion explores how knowledge graphs can map organizational structures, processes, and relationships to drive successful change initiatives. Vadym shares insights from his experience at Nestle and other enterprises on identifying effective change agents through network analysis.

3. The Science of Change Management
We explore the fascinating research behind successful organizational change, including the importance of network topology in selecting change agents and why traditional approaches often fail. Vadym explains why focusing on early adopters rather than innovators leads to more successful transformations.

4. Combining Knowledge Graphs with Generative AI
The conversation examines how enterprises can leverage both knowledge graphs and large language models to create more reliable and factual AI systems, using the metaphor of having both a master librarian and universal interpreter at your disposal.

INSIGHTS:

TOOLS AND TECHNOLOGIES MENTIONED:

CONTACT INFO:

CHAPTERS
00:00 Introduction and Background
03:15 From Ketchup Factories to Data Science
07:30 Evolution of Graph Applications
12:45 Understanding Knowledge Graphs
18:20 Combining Knowledge Graphs with Generative AI
23:40 Enterprise Change Management
31:15 Network Analysis for Change Agents
38:50 Rogers' Innovation Adoption Theory
45:30 Knowledge Graphs and Misinformation

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In this episode, we explore the evolution of AI in product management and the crucial balance between automation and human judgment with Jorge Alcantara, a founder of Zentrik.ai and a veteran in AI implementation and product development. Jorge shares his journey from early chatbot development to founding Zentrik, offering unique insights into the future of product management in the AI era.

TOPICS DISCUSSED:

1. The Evolution of AI Implementation

2. The PM Paradox

3. Human-Centric AI Development

4. The Future of Product Management

INSIGHTS:

1. Product management is becoming the skill of the future as development gets commoditized.

TOOLS AND TECHNOLOGIES MENTIONED:

CONTACT INFO:

CHAPTERS

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In this episode, we dive into the fascinating world of quantum computing with Hila, a quantum computing researcher at Siemens focusing on hardware-software co-design. From her unexpected journey from aspiring surgeon to quantum computing expert, Hila brings unique insights into the future of this transformative technology and its real-world applications.

TOPICS DISCUSSED:

1. Quantum Computing Fundamentals

2. Current Challenges and Solutions

3. Technical Implementation

4. Practical Applications

5. Social Impact and Responsibility

INSIGHTS:

1. Quantum computers are best suited for specific tasks rather than general-purpose computing.

TOOLS AND TECHNOLOGIES MENTIONED:

CONTACT INFO:

CHAPTERS

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In this episode, we dive deep into the world of local AI models and their creative applications with Shahbaz Mansahia, an ML engineer, an IEEE author, researcher and advocate for democratizing AI technology. Shahbaz shares his unique perspective on running AI models locally, making AI accessible to art students, and using technology to address representation gaps in art.

TOPICS DISCUSSED:

1. Local Models

2. AI in Education

3. AI and Artistic Creation

4. Technical Implementation

INSIGHTS:

1. One can break dependency from any model provider with local models.

TOOLS AND TECHNOLOGIES MENTIONED:

CONTACT INFO:

CHAPTERS

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AI Ketchup - Catching up on the WHY behind tech products!

In this podcast, we're re-engineering human-centered product creation, focusing on AI solutions that matter. Think of this as your opportunity to step into the shoes of people solving real-world problems.

In the early days of cars, building engines wasn't easy, but it was a problem with a clear direction—unlike the challenge of defining traffic rules and places for roadways. Today, AI's technical barriers are falling, but the real focus is on what to build and why. Seven years ago, when I started exploring teh area of data science and machine learning, AI was confined to labs and very progressive universities. Now, with democratized tools, the HOW is simpler—it's time to master the WHY.

In each episode, founders share their decision-making process and builders reveal their methods, while you join the conversation thinking through how to solve for specific human needs. Join us twice a month—let's build what truly matters.