Florentine.ai ensures key/value based isolation of data, i.e. user-based data separation.


The internal transformation layer modifies aggregation pipelines to ensure secure data separation without relying on the LLM to do so.

Use the Required Inputs feature to define which fields will be added to the pipeline.

Only share your data structure with the LLM, not the data itself.



Thus Florentine.ai protects your IP (Intellectual Property) while still enabling the benefits of AI generated data query.

Do semantic searches with automated embedding creation on your data for advanced RAG (Reality Augmented Generation) support.


Define score thresholds to fine tune your vector searches and return only the data you expect.

Choose the structured response you need from the three options aggregation, aggregation result and natural language answer.

Retrieve the aggregation only or let Florentine.ai directly execute the aggregation and retrieve the aggregation result.

Florentine.ai can also return the result as a natural language answer.

Use the Florentine.ai MCP Server or the Florentine.ai API to integrate natural language to MongoDB data in your project.

Integration is straightforward and well-documented. In case of any issues we will be glad to assist you.    

Complex questions can easily be answered with various lookup stages spanning across multiple collections.

Lookups work out of the box and are created automatically.

For edge cases where connected fields are not evident at first sight, you can manually assist the AI  by interconnecting collections by its keys via the Mappings feature.

Better control over behavior for specific use cases by adding custom prompt instructions.


Custom prompts can be added on database or collection level.