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.

Learnings from sponsoring a hackathon.

How to quickly get your first prediction done within 3 commands.

Comparison of predictive queries and supervised ML models workflow, architecture and scaling/accuracy-wise

Step-by-step practical example of implementing machine learning with Robot Framework.