2018 → 2022
Bring AI to the data.
MindsDB started with one clear pivot: instead of forcing teams to move their data to wherever the model lived, bring the model to wherever the data already lived. Inside the database. With the same SQL ergonomics the team already used. No exports, no separate ML stack, no specialist hire required to ship a first prediction.
The product fit was natural enough that the name made the architecture obvious: the work happened next to the database, so the suffix was DB. At the time, most useful ML data was sitting in databases — Postgres, MySQL, MongoDB, Snowflake — so the project lived there too.