The v2 engine is a new predictive database that holds facts, text, vectors, and relationships in one store and answers calibrated, explainable queries about the unknown. It is in public beta, running alongside v1. This post is concrete on purpose: here is what it 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.

A new architecture for B2B SaaS

Buyers ask whether a predictive database can hold up at SaaS scale. Here are the numbers from a 10-million-row invoice routing benchmark, run end-to-end through Aito's HTTP API the way production traffic actually hits it: low-hundreds-of-milliseconds predict latency, sub-linear scaling from 1k to 10M, and what the cold-start looks like before the cache warms.

A predictive database lets you query predictions (classifications, recommendations, missing values) from structured data, using the same query interface you use for data retrieval. No training step.

Databases are absorbing AI capabilities. A taxonomy of the four categories emerging in 2026: vector databases, ML-in-database platforms, LLM-augmented databases, and predictive databases.

Aito uses a layered system of Bayesian priors to make accurate predictions even when training data is sparse. This is how it works, why it matters for cold-start scenarios, and what the numbers look like.

We raised API limits by 25x on Dev and removed them entirely on Prod. The free Sandbox trial window is back to 30 days. Here is what changed and why.

Starting October 1, we’re updating our pricing and enhancing API access limits to continue delivering top-quality services. These changes reflect increased operational costs and our commitment to improving our platform. We appreciate your understanding and continued partnership.