How Startups Build Agentic Systems: A New Approach to Data

AI-driven startups face a growing challenge: traditional databases struggle to keep pace with the dynamism of agentic systems. Rigid schemas in relational databases demand manual migrations with every data structure change, slowing development and increasing errors. Three startups—Huntr, Modelence, and Tavily—have found a solution by adopting MongoDB Atlas, a platform that supports vector search, hybrid queries, and automatic scaling.

Modelence, a platform for building AI applications, initially faced incompatibility between traditional backend systems and AI requirements. Company founder Aram Shatakhtsyan notes that MongoDB Atlas enabled the consolidation of all data—including live data streams and vector search—into a single platform. This is critical for AI agents, which require a unified system to operate without integration-related errors.

Tavily, a real-time search API, uses MongoDB Atlas to manage millions of API keys, track data relevance, and scale workloads. Tomer Weiss, Head of Data, emphasizes that MongoDB’s flexible schema allowed the company to adapt to new demands without complex migrations—a crucial advantage in the fast-evolving AI industry, where adaptability is key.

Huntr, a resume-building platform powered by AI, stores candidate career data in MongoDB’s document format. This enables the system to process deeply nested and variable data while leveraging hybrid and vector search to optimize resumes for specific job postings. The startup’s team highlights that integrating search and vector technologies into a single platform accelerated development and improved AI model accuracy.