
Query your data & get instant predictions—no model training required.
Build predictive applications without ML complexity. Aito's predictive database lets you query predictions, recommendations, and insights using familiar SQL syntax.
Live demo → inspect logic → try with your data
{
"from" : "invoices",
"where" : {
"Description": "AWS Cloud"
},
"predict" : "Processor",
"limit": 1
}{
"hits": [{
"$p": 0.908,
"Department": "IT",
"Name": "Carol White"
}]
}Real use cases with actual query syntax and production results
{
"from": "prompts",
"where": { "prompt": "Which payment methods do you provide?" },
"predict": "answer"
}

Run real queries against our demo database. No sign-up required.
Predict the GL code for a cloud services invoice
{
"from": "invoices",
"where": {
"ProductName": "Cloud Services (AWS/GCP)",
"TotalAmount": 5000,
"InvoiceType": "Service"
},
"predict": "GLCode"
}Start with predictive analytics, progress to human-in-the-loop assistance, and automate workflows end-to-end — using the same query language.
Make decisions from data — instantly.
Segment customers, forecast outcomes, monitor quality. Start with analytics, evolve into predictive KPIs.
Suggest the next best action — with confidence.
Smart search, recommendations, autofill and routing suggestions. Human stays in control; Aito boosts accuracy and speed.
Performance metrics measured on standard cloud infrastructure
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.
Read more
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.
Read moreDatabases 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.
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