Jezz Kelway leads a team focused on delivering Postgres-based analytics and AI solutions at EDB.
Postgres has reemerged in popularity with greater relevance in the AI era than ever before due to its robust architecture, native support for multiple data types, and extensibility by design.
Retrieval-Augmented Generation (RAG) flows are gaining significant popularity and momentum, with good reason. RAG flows are enabling access to information in ways that facilitate the human experience.
Due to data quality being an AI differentiator, the accuracy of the generated responses of a RAG application will always be subject to the quality of data that is being used to train and augment the output.
Building an AI/RAG solution will often utilize a vector database as these applications involve similarity assessments and recommendations that work with high-dimensional data.
While Postgres does not have native vector capability, pgvector is an extension that allows you to store vector data alongside the rest of your data.
EDB Postgres AI Pipelines extension is an example of how Postgres is playing a key role in shaping the ‘data management’ part of the AI application story.
EDB Postgres AI brings cloud agility and observability to hybrid environments with sovereign control.
Postgres excels as the backbone for AI-ready data environments, offering advanced capabilities to manage sensitive data across hybrid and multi-cloud settings.
EDB Postgres AI helps elevate data infrastructure to a strategic technology asset by bringing analytical and AI systems closer to customers’ core operational and transactional data.