This episode explores the rapid evolution of AI tools, their practical applications in business, and how they can exponentially increase productivity and service quality. Jack and Craig discuss recent innovations like Claude design, AI integration in Outlook, and field management systems, emphasizing the importance of adoption and strategic implementation.

In this episode, Craig and David explore the latest in AI technology, including AI development, tools, and practical applications for business and content creation. They share insights on building AI-driven workflows, integrating AI with legacy systems, and future trends in AI innovation.

This episode features a deep dive into AI's transformative impact on real estate and property management, with Kris Garin sharing innovative tools, strategies, and insights on leveraging AI for operational efficiency and growth.

This episode dives into the real challenges of adopting AI inside a business—from broken workflows and messy builds to the human side of change. The team explores why AI projects can fail, how to structure systems the right way, and what it actually takes to get an entire company bought into using AI effectively.

In this episode, Steve White shares his journey from software development at Microsoft to creating an innovative underwriting console for real estate. Discover how AI and automation are transforming mortgage underwriting, property analysis, and market intelligence, enabling faster, more accurate decisions for lenders and investors.

In Episode 2 of 4kd.ai, Jack, Craig, and David dive into real-world applications of agentic AI and what it actually looks like to build with these tools day-to-day. From replacing legacy software with custom AI workflows, to building dispatch systems, scraping and analyzing market data, and even running deep investigative research, this episode is all about execution—not theory.
They break down how they’re using tools like OpenClaw, MongoDB, and local models to automate workflows, reduce manual work, and uncover insights that would have taken hours (or days) before. Along the way, they also talk through what’s working, what’s not, and how they’re thinking about building scalable AI systems inside real businesses.