Engineering teams worldwide are developing AI-focused applications or adding AI features to existing products. The AI development ecosystem is evolving, speeding up the prototyping of these applications. However, deploying AI applications into production remains a complex process. Modern AI stacks require LLMs, embeddings, vector search, observability, new caching layers, and constant adaptation to the rapidly changing landscape. The data layer has increasingly become both the foundation and bottleneck in AI app production.
MongoDB is expanding its core document database into a comprehensive AI-ready database platform with integrated capabilities for operational data, search, real-time analytics, and AI-driven data retrieval. The company recently acquired Voyage AI to offer its users accurate and cost-effective embedding models and rerankers.
Fred Roma, a veteran engineer and currently the SVP of Product and Engineering at MongoDB, joins the show with Kevin Ball. They discuss the state of AI application development, the role of vector search and reranking, schema evolution in the LLM era, the Voyage AI acquisition, how data platforms must evolve to keep up with AI’s rapid pace, and more.
Full Disclosure: This episode is sponsored by MongoDB.
Kevin Ball, also known as KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
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