Built for Enterprise Scale and Developer Productivity
Replace complex ML infrastructure with simple database queries. Get enterprise AI capabilities without the enterprise complexity.
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
A real prediction query that executes in ~150ms
{
"from": "invoice_data",
"where": {
"Item_Description": "Packaging design",
"Vendor_Code": "VENDOR-1676"
},
"predict": "Product_Category"
}
This query creates a custom model considering all relevant patterns in your data, makes the prediction, and discards the model - all in milliseconds
Traditional ML vs. Predictive Database Architecture
See how Aito eliminates the complexity of traditional machine learning infrastructure
Traditional ML: Complex Pipeline
Aito: Single Database
Technical Implementation:
Enterprise Performance Metrics
Production-proven performance across enterprise deployments
Performance and Accuracy Benchmarks
Third-party validated performance metrics from production deployments
Prediction Performance at Scale
Sub-200ms performance maintained even at 10M+ records. Memory: 60-250MB scaling linearly. 100% reliability with zero errors across all test scenarios.
ML Model Accuracy Comparison
Aito vs traditional ML algorithms across standard datasets
Aito ranks 3rd out of 8 methods tested with 0.96 mean accuracy. Competitive performance with 20-168ms response times vs traditional ML requiring training pipelines.
Beyond Traditional ML: Automatic Context Recognition
Aito's intelligent inference automatically matches entities through metadata and free text, eliminating the rigid feature engineering requirements of traditional supervised learning
Automatic Entity Matching
Recognizes names, departments, and entities in free text without explicit configuration
Metadata-Driven Intelligence
Leverages all available metadata fields automatically for richer predictions
Query-Time Inference Engineering
Dynamically optimize predictions through human-guided queries, filtering targets, and selective feature conditioning
Traditional Supervised Learning
- Requires explicit feature engineering for every field
- Cannot handle unknown entities without retraining
- Fixed model behavior - no query-time optimization
- Separate NLP pipeline for text extraction
- Manual configuration of all relationships
Aito Predictive Database
- Automatic feature discovery from all available data
- Handles new entities through similarity matching
- Dynamic query-time optimization and filtering
- Integrated text understanding
- Self-discovering relationships
Invoice Processing Example
Traditional ML
Requires separate NLP model to extract entities, manual feature engineering to link Bob to employees table, and retraining if Bob is new
Aito Approach
Automatically extracts 'Bob Johnson' and 'IT', matches to employee records, finds similar patterns even for new employees
LLMs vs Predictive Databases: Why Automation Requires Reliable Confidence
While LLMs excel at reasoning and explanation, only predictive databases provide the reliable confidence metrics essential for business automation
LLMs Excel At
Limitations:
Predictive Databases Excel At
Limitations:
Invoice Processing Automation Example
LLM Approach
"This invoice from CloudTech for $2,400 appears to be for cloud infrastructure services, so I'd suggest coding it to IT expenses"
Predictive Database Approach
"Analyzes 50,000 similar vendor invoices and predicts GL code 6100-Cloud-Services with 96% confidence"
The Hybrid Solution
The most powerful enterprise AI combines both approaches strategically
Why Engineering Teams Choose Aito
Eliminate MLOps Complexity
No pipelines, no model deployments, no infrastructure to maintain. Just query predictions like data.
Real-Time Learning
Models adapt automatically as data changes. No retraining pipelines or concept drift management.
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.
Significant Total Cost of Ownership Reduction
Estimated cost comparison for enterprise prediction and classification use cases (based on industry analysis)
Traditional ML Stack
Aito Predictive Database
Technical Resources for Architects
Predictive Queries vs ML Models - Technical Analysis
Deep dive into lazy learning architecture and performance characteristics
API Documentation
Complete API reference with code examples
Getting Started Guide
Step-by-step technical implementation guide
LLMs vs Predictive Databases - Complete Analysis
Comprehensive comparison for choosing the right AI approach for structured data
Ready to Eliminate ML Infrastructure Complexity?
Join CTOs at Posti, GridPane, and other leading companies who've simplified their AI strategy with Aito
Address
Episto Oy
Putouskuja 6 a 2
01600 Vantaa
Finland
VAT ID FI34337429
