Why We Still Need Relational Databases in the Era of AI

Compared to scanning entire JSON documents that may contain a lot of unrelated information, relational databases offer several advantages:

  • Easy to query — SQL makes it straightforward to retrieve exactly what you need without parsing large, nested structures.
  • Great for analytics — With structured schemas, indexing, and joins, relational databases excel at analytical workloads and complex data relationships.
  • Cost-efficient for AI workflows — AI tools often only need specific fields. Relational tables let you access a single column or row directly, reducing compute cost versus scanning whole documents.

    • Our Technology

    Built on the same advanced architecture as AgenticConversationDataProcessing.com, our research platform provides:

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    Research Applications

    AI & Cognitive Science

    Iterative annotation and flatten of JSON into a traditional relational database

    Linguistics & Communication

    Explore discourse structure, persuasion, and conversation intent.

    Human-Computer Interaction

    Process dialogue data from human-agent experiments.

    Machine Learning Datasets

    Generate structured datasets from multi-agent interactions.