Build with EDB Postgres^® AI: A 5-Part How-to Demo Series

EDB Postgres AI is an advanced data platform that combines the reliability of PostgreSQL with next-generation AI and analytics capabilities. It allows organizations to build, scale, and optimize workloads for AI applications—such as virtual assistants, analytics agents, and multimodal search—using the same enterprise-grade PostgreSQL foundation they already trust. With integrated AI pipelines, a low-code/no-code GenAI application and agent builder, lakehouse analytics, and governance tools, EDB Postgres AI delivers scalability, security, and performance for complex data-driven use cases.

PostgreSQL, especially when enhanced through EDB Postgres AI, provides a flexible and open foundation for building AI applications. Developers can integrate application and agent builders, vector search, and advanced extensions to build and deploy intelligent assistants, automate analytics, and process unstructured data. Because EDB Postgres AI extends standard PostgreSQL, teams can use familiar technology to scale AI workloads across hybrid or multi-cloud environments while maintaining full compatibility and governance.

EDB Postgres AI includes powerful governance features with its Lakehouse Connector, enabling secure metadata management and compliance across diverse data sources. It also supports multimodal search—combining structured, unstructured, and visual data—and simplifies RAG application development through the AI Factory’s AI Pipelines and Vector Engine. This makes it ideal for use cases including recommendation engines; AI copilots; and intelligent document retrieval where data must be accurate, governed, and instantly searchable.

EDB Postgres AI helps data engineers and IT leaders unlock real-time insights across complex environments. It simplifies data integration from traditional databases, data lakes, and streaming sources while offering built-in scalability for AI and analytics workloads. The result is faster time to insight, lower total cost of ownership, and the ability to unify transactional and analytical processing within a single PostgreSQL-based platform.

Using EDB Postgres AI Analytics Accelerator, users can natively query Apache Iceberg and Delta Lake tables directly from PostgreSQL, managing schema evolution and metadata through our Lakehouse Connector for consistent governance. This integration enables seamless analytics across structured and semi-structured data without the need for complex ETL processes.

To analyze time-series data efficiently, EDB Postgres AI leverages the Bluefin extension, which enhances PostgreSQL with specialized indexing and compression for high-volume temporal data. Best practices include partitioning tables by time intervals, using continuous aggregates for real-time metrics, and optimizing query performance with parallelization. These capabilities make it ideal for financial, IoT, and performance analytics workloads.

AI agents developed with EDB Postgres AI Factory’s Agent Studio can autonomously analyze business data, detect patterns, and recommend next steps. For example, analysts can “chat” with an AI agent to uncover customer trends or identify upsell opportunities. Because these agents operate directly on well-governed Postgres data and can be deployed on-prem, in the cloud, or in hybrid environments, insights remain secure, explainable, and aligned with enterprise compliance standards.

Yes. With EDB Postgres AI, organizations can combine text, image, and metadata search capabilities into a single AI-powered system. This multimodal search approach enables use cases such as identity verification and fraud detection by correlating data across multiple modalities. By running these workloads on EDB Postgres AI, teams benefit from enterprise-grade performance, extensibility, and robust AI integration.