I want to attempt an explanation as it seems a lot of folks here didn't grok the very terribly written README

"Auto Ontology" is sort of a poor name.

Here's the problem:

Your company has data scattered across multiple types of databases. People who make decisions want to ask questions about that data.

The old way is they would go to a Data Analyst, and say, "Get me a report on so and so", and it was the Analyst's job to iron out the right series of queries and one-off scripts to generate the needed info.

With a system like this, the idea is that all your database models have been introspected and stored in a central metadata catalogue.

When a question is asked like "What were the top selling items in the month of May?", the metadata catalogue tells you "Products are stored in FOO_DB" and "Product_Sales are stored in BAR DB" and so you go and fetch the right data from the right place and do whatever in-memory calculations you need to do.

What the system provides is the implementation code for:

- Introspecting each of your DB's

- Aggregating model + metadata information in a central, virtual DB (VDB)

- Generating dialect-specific SQL from natural language queries and routing them to the right DB for the underlying table

- Combining results in-memory when a natural language query requires multiple datasources

- Handling proper permissions, etc

A GOOD system for doing this also integrates with your external API sources (OpenAPI, GraphQL, gRPC, whatever) and also documentation sources (cough, I work on this, cough).