Aito · the predictive database
Calibrated predictions from your live data, in one query.
Generative AI gave software language. Aito is the predictive database that gives it judgment: instant, calibrated decisions learned from your own business data, with a confidence score you can audit and no model to train. The same predictive operators serve your users, your workflows, and your agents.
docker pull ghcr.io/aitohq/aitoFree for development. Production license at usage scale.
See the demos → recognize the pattern → talk to engineering
{
"from" : "invoices",
"where" : {
"Description": "AWS Cloud"
},
"predict" : "Processor",
"limit": 1
}{
"hits": [{
"$p": 0.908,
"Department": "IT",
"Name": "Carol White"
}]
}The intelligent application
Intelligent = Generative + Predictive
Predictive features on every screen
See Aito in Action
Views from the live demos, with the query behind each one.
Invoice GL Coding
{
"from": "invoices",
"where": {
"customer_id": "CUST-0000",
"vendor": "Kesko Oyj",
"amount": 4220
},
"predict": "gl_code",
"select": ["$p", "feature", "$why"]
}
The faculty most AI stacks are missing
LLMs gave your agent reasoning. RAG gave it memory. Aito gives it intuition.
The live agent at agent.aito.ai calls Aito ops as tools: win odds, effort, references, the outreach that books the meeting, all calibrated from the firm's own history. It drafts the action and gates the send. Numbers an LLM can't invent.
From Insight to Automation — Analyze → Assist → Automate
Three ways to make every screen of your product intelligent: surface the numbers your users can't compute (Analyze), narrow and ground their choices (Assist), and let decisions run when the prediction is confident (Automate). The same predictive operators serve a product UI and an agent's toolbox alike.
Analyze
Make decisions from data — instantly.
Segment customers, forecast outcomes, monitor quality. Start with analytics, evolve into predictive KPIs.
Assist
Suggest the next best action — with confidence.
Smart search, recommendations, autofill and routing suggestions. Human stays in control; Aito boosts accuracy and speed.
Apply all three across every screen — that's a predictive application.
Built for Your Industry
The same predictive layer, packaged per vertical, in production since 2018.
Try a Real Query Against the Demo Data
For developers — run real predictive queries against Aito's demo database, no sign-up. The shape of the API, visible in seconds.
Invoice Processing
Predict the GL code for a cloud services invoice
{
"from": "invoices",
"where": {
"ProductName": "Cloud Services (AWS/GCP)",
"TotalAmount": 5000,
"InvoiceType": "Service"
},
"predict": "GLCode"
}Or run it locally
Same predictive database, in a container on your laptop. No signup; you pin your own API key, and every request sends it.
docker pull ghcr.io/aitohq/aitoexport AITO_KEY=$(openssl rand -hex 24)docker run -d --name aito -p 127.0.0.1:9005:9005 -v aito-state:/io/state -e READ_WRITE_APIKEY="$AITO_KEY" -e APIKEY="$(openssl rand -hex 24)" ghcr.io/aitohq/aitountil curl -sf localhost:9005/version >/dev/null; do sleep 1; done
curl -X PUT localhost:9005/api/v2/schema/invoices \
-H "x-api-key: ${AITO_KEY:?set AITO_KEY first}" -H 'content-type: application/json' \
-d '{"type":"collection","columns":{"vendor":{"type":"String"},"description":{"type":"Text","analyzer":"english"},"account":{"type":"String"}}}'
curl localhost:9005/api/v2/data/invoices/batch \
-H "x-api-key: ${AITO_KEY:?set AITO_KEY first}" -H 'content-type: application/json' \
-d '[{"vendor":"AWS","description":"cloud hosting and compute","account":"6510 Cloud"},{"vendor":"Google Cloud","description":"cloud compute usage","account":"6510 Cloud"},{"vendor":"Slack","description":"team messaging subscription","account":"6520 Software"},{"vendor":"Atlassian","description":"Jira annual subscription","account":"6520 Software"},{"vendor":"Finnair","description":"flights to a customer visit","account":"6310 Travel"},{"vendor":"Scandic","description":"hotel for a conference trip","account":"6310 Travel"}]'
curl localhost:9005/api/v2/_predict \
-H "x-api-key: ${AITO_KEY:?set AITO_KEY first}" -H 'content-type: application/json' \
-d '{"from":"invoices","where":{"vendor":"Datadog","description":"monitoring subscription"},"predict":"account","limit":3}'- 10,000 rows per table, 50,000 rows total — free tier
- Same query API as cloud: no code changes when you scale
- Runs on your laptop, in your CI, in your VPC
In production
20–168ms response (P95 < 200ms) · 10M+ records · 90–98% accuracy · 80% GL coding fully automated at end customers · Nordic enterprise AP since 2018 · 99.9% uptime SLA
Benchmarked vs the standard stack
~17× smaller prompts, same shortlist (measured on v2, 20 tickets) · an order of magnitude faster than an LLM call (measured on v1) · structured match where embeddings pick the wrong customer 86% of the time (measured on v1)
One engine, no models, nothing to forget
Upload and Query
Continuous Learning
Multiple Predictions
Tobias Vogel,
Chief Revenue Officer @Gridpane
Built for Enterprise Scale and Developer Productivity
Discover how Aito's predictive database architecture eliminates MLOps complexity while delivering enterprise-grade performance, security, and 90% cost reduction compared to traditional ML infrastructure.
Your Postgres tools work unchanged: predict() inside a SELECT, live at sql.aito.ai
See what customers are doing with Aito
What developers are building
How Aito Scales to Millions of Rows
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
Top 3 insights from using RPA+ML to automate invoice processing
ML in RPA 1) can be very rewarding, 2) it requires a different mindset and 3) it can be easy
Read more
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01600 Vantaa
Finland
VAT ID FI34337429
