The Database That Thinks
Predictive queries replace machine learning models, infrastructure, and specialized teams
Aito transforms AI from complex infrastructure into simple database queries. Your existing development team can implement intelligent features in hours using familiar SQL-like syntax, without MLOps pipelines, model training, or data science expertise.
Query-Time Intelligence: The Technical Innovation Behind Aito
Aito is a specialized database that performs statistical inference at query time, eliminating the need for pre-trained models
Lazy Learning Architecture
Unlike traditional ML that trains models upfront, Aito creates query-specific models on demand
Query-Time Model Creation
Each prediction query triggers real-time feature selection, concept learning, and Bayesian inference
No Model Deployment
Models don't exist until queried - eliminating deployment, versioning, and drift issues
Unified Statistical Engine
Same Bayesian foundation powers predictions, recommendations, and search
See It In Action
Here are 3 live queries that demonstrate Aito's predictive capabilities
Invoice Classification
Predict product category from invoice description
{
"from": "invoices",
"where": {
"ProductName": "Cloud Services (AWS/GCP)",
"TotalAmount": 5000,
"InvoiceType": "Service"
},
"predict": "Processor",
"select": [
"$p",
"Name",
"Role"
]
}Experience Aito's Full Capabilities
Explore complete applications built with Aito's predictive database. See personalized search, AI assistants, and document processing in action - all implemented in hours, not months.
Personalized Search
Same search query returns different results for Alice, Larry, and Veronica based on their preferences. See how AI personalizes "milk" searches for dietary restrictions.
Conversational AI Assistants
Shopping assistants help customers find products while admin assistants provide business insights. Built with the same predictive database - no separate NLP infrastructure.
Intelligent Document Processing
AI predicts GL codes, extracts payment terms, and detects anomalies from invoice data. The same system Posti uses to process 3,000+ invoices monthly with 95% accuracy.
Why Aito vs Traditional ML, LLMs, and Custom Development
Comprehensive comparison for technical decision-makers choosing AI approaches for structured data use cases
Traditional ML Infrastructure
Architecture:
Large Language Models
Strengths:
Aito Predictive Database
Architecture:
Invoice Processing Automation: Invoice processing: 'Cloud services for Bob Johnson's IT infrastructure project' → GL Code prediction
Traditional ML
Build NLP pipeline → Extract entities → Train classification model → Deploy MLOps infrastructure
LLM Approach
Send invoice text to LLM with few-shot examples
Aito Approach
Upload invoice data → Query for prediction with confidence score
The Strategic Hybrid Approach
Most enterprises benefit from combining approaches strategically
Enterprise Performance Metrics
Production-proven performance across enterprise deployments
Performance Benchmarks & Production Validation
Key metrics from production deployments and third-party testing
Key Performance Metrics
Production Validation
Real customer deployments in production environments
Why Engineering Teams Choose Aito
Eliminate MLOps Complexity
No pipelines, no model deployments, no infrastructure to maintain. Just query predictions like data.
Multi-Purpose Intelligence
One system handles predictions, recommendations, search, and analytics for structured data scenarios.
Developer Productivity
Existing development team can implement AI features. No specialized ML expertise required.
Built for Enterprise Requirements
Flexible deployment options and data governance for regulated environments
Deployment Flexibility
Deploy in your own cloud infrastructure or use EU-hosted managed service
Data Sovereignty
Complete control over data location and access with no vendor lock-in
GDPR Compliance
Built-in compliance features for European data protection requirements
Total Cost of Ownership Analysis
Estimated cost comparison for enterprise prediction use cases
Traditional ML
Aito Approach
Cost Savings Breakdown
Address
Episto Oy
Putouskuja 6 a 2
01600 Vantaa
Finland
VAT ID FI34337429
