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Jeremy Kelway, VP of Engineering for Analytics, Data, and AI at EDB – Interview Series

  • 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.

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