Software is now expected to be intelligent, and most applications are missing the part that would make them so: a predictive core that knows their own data. Aito v2, generally available today, is the predictive database built to be that core. It speaks SQL to the systems you already run, holds vectors and relationships for your LLMs and agents, and our own applications run on it.

The first wave of agentic AI tried to build autonomous software out of language models alone. That was the right first move for language and the wrong design for action. Software that acts needs reasoning for the exceptions and intuition for the rule, working as peers, and it needs both to sit on one foundation. Here is the paradigm, the architecture it implies, and how Aito runs it without a Frankenstein stack.

Written in August 2026, when v2 was in public beta. v2 became generally available on 21 September 2026, so this post is the record of the beta rather than the current state. It is concrete on purpose: here is what the engine does, here are queries you can run, and here are the numbers, including the ones that do not flatter us.

The efficient market hypothesis says you cannot beat the market. Graham, Buffett, and a lineage of growth investors spent decades saying otherwise. So I ran the experiment: grade 250 S&P 500 companies on value, quality, and growth signals, point-in-time, and let a predictive database score all three philosophies against twelve years of real outcomes. The answer surprised the value investor who built it.