they've also somehow modified Lucene's search algorithm to perform some ML calculations, but it's not clear what they've implemented. still not a "new database category" though.

Founder here, Aito has a custom database, that has been bottom up optimized for statistical operations.

This let's Aito create models at spot to answer the predictive queries

Could you share any of the thought process that led to making this from the ground up rather than extending/layering on top of an existing DBMS (or, conceivably, multiple DBMSes)?

Was there indexing and storage engine considerations? Was it a lack of interface support for this kind of thing? Marketing? I could see a lot of arguments either way and wondered what convinced you.

It's always auspicious to start a software project in Finland, all the best of luck on this! The site looks great.

Aito builds a model for predictive queries in millisecond scale. It requires heavy optimizations and preparations in DB to reach the performance. The indexes are optimized for statistics, and there are extra datastructures not found in normal DBs.

The ML is also implanted inside the database to minimize various overheads, and to have direct access to data & invested. if you need to do thousands of statistical operations in 10ms, just IPC can become a huge overhead. You want to put data & math in same process.

Overall, its all based on tight AI+DB integration to enable the instant modeling.

can you share details about the "instant modeling" capability? while simple copula's and correlation matrices may be calculable in milliseconds, larger models are likely to have more performance considerations and training latencies.

It's done for discrete data. Operations on such data can be optimized to pretty extreme level. Aito scales to around million rows and million features before slowing down too much.

We believe we can make it scale to 10m or 100m rows in the future. Maybe more

got it, any plans on supporting models requiring backprop/gradient descent?