I would really like to see (and work for) a company that is building novel understanding of actual data and schemas with LLMs. Characterizing data and a limited number of transforms for an LLM should produce much more reliable tools than just piping direct text to a non enhanced LLM. Has anyone seen companies where they are doing this?
It will be difficult because of how organizations work. For example, finance and accounting people only care about shipped sales because that's when revenue is recognized whereas marketing and supply chain people think of demand sales (when order was placed). So you would need something to be able to interpret the difference depending on the audience or train the audience to be clear in their questioning.
Same goes for calendar vs. fiscal year for companies that have different fiscal and calendar begin dates. Something as simple as "2023 YTD" will mean different things depending on the audience within an organization.
When Veezoo connects to a database / dwh for the first time, an initial Semantic Layer / Knowledge Graph gets built automatically based on the data itself. We try to recognize how the columns link to other tables, try to identify units, and other semantic information e.g. if something is a "Location" or a "Country" and so on.
The whole conversational "plain english" querying then operates on top of the semantic layer, ensuring business logic (and other governance topics) are always respected.
That's what we're doing with Definite[0]. We spin up Cube[1] for all our customers and the results vs. directly generating SQL are much better. Cube has some other really nice out of the box features too (e.g. caching).
