How Databases and AI Are Evolving Together

AI and data are converging faster than ever before. In this episode of Full Tech Ahead, host Amanda Razani sits down with Szymon Kulec, Lead Developer Advocate at RavenDB, to discuss how the future of data management and AI integration is unfolding. Kulec explains how RavenDB’s native AI agents, vector search, and secure data architecture empower developers to move faster while maintaining compliance and governance.

Summary

Szymon Kulec brings his experience as a computer scientist and developer advocate to explore how RavenDB, a NoSQL distributed database, is bridging the gap between data systems and artificial intelligence. He explains that RavenDB is designed to make data handling “as boring as possible” for developers — reliable, seamless, and automatically managed.

The conversation dives into RavenDB’s native AI agents, which operate close to the data for both performance and security. These agents integrate safely with AI models through the Model Context Protocol (MCP), ensuring data is queried efficiently and responsibly. Sensitive information, such as payroll or health data, can be tightly restricted to prevent prompt injection, data leaks, or jailbreaks.

Kulec highlights the importance of real-time data in powering modern AI applications. With tools such as vector search, semantic queries, and RAG (retrieval-augmented generation), RavenDB enables AI models to access the most relevant, up-to-date data without manual intervention. This balance of openness, safety, and accessibility gives organizations the agility to maintain control and comply.

Looking to the future, he predicts that the integration between databases and AI will become even more automated. Developers will rely less on writing extensive glue code and more on configurable systems that “just work.” As AI becomes embedded into every layer of data infrastructure, tools like RavenDB will help enterprises remain efficient, secure, and compliant.

Quotes

• “We put the agents as close to data as possible and as safe as possible.”
• “With RavenDB, if you forbid it to query certain data, it will be strictly forbidden. The contract will not be breached.”
• “RavenDB tries to be as boring as possible for you as the end user, which is a good thing — it just works.”

Takeaways

• RavenDB is a NoSQL database offering powerful indexing, sharding, and distributed data capabilities with integrated AI features.
• Native AI agents within RavenDB interact directly and safely with data, ensuring compliance with privacy and security requirements.
• Using open standards like the Model Context Protocol enables controlled AI behavior, reducing risks of data breaches or prompt injections.

Timestamps

[00:00] Introduction — Amanda Razani and Szymon Kulec
[00:28] Background and overview of RavenDB
[01:25] What makes NoSQL different from traditional SQL databases
[02:33] How RavenDB integrates native AI agents
[04:50] The importance of real-time data and retrieval-augmented generation
[06:59] Balancing developer speed with data safety and compliance
[09:22] The evolving relationship between AI and databases
[11:14] Real-world example: building an HR app in under 700 lines of code
[12:00] Key takeaway: use tools wisely and automate whenever possible

Links / Resources

Website: RavenDB - https://ravendb.net

 LinkedIn: Szymon Kulec - https://www.linkedin.com/in/szymon-kulec/
Podcast: Full Tech Ahead — Hosted by Amanda Razani

Find Amanda Razani on LinkedIn. https://www.linkedin.com/in/amanda-razani-990a7233/