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 predictions like data: `SELECT * FROM predictions WHERE ...`
No model training, deployment, or maintenance required
20-168ms response times scaling to 10M+ records
Real-time learning without pipeline complexity

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

Millisecond-scale model creation using specialized indexes for microsecond statistical operations

No Model Deployment

Models don't exist until queried - eliminating deployment, versioning, and drift issues

Stateless inference means no model artifacts to manage or maintain

Unified Statistical Engine

Same Bayesian foundation powers predictions, recommendations, and search

Text-book Bayesian approaches generalized across all query types

See It In Action

Here are 3 live queries that demonstrate Aito's predictive capabilities

Invoice Classification

Predict product category from invoice description

Aito Query
{
  "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.

Interactive Demo
Smart search personalization demo

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.

• Real-time personalization• Context-aware ranking• 20-168ms response times
Live AI
AI shopping assistant demo

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.

• Natural language queries• Business intelligence• Contextual recommendations
Enterprise Ready
Invoice processing automation demo

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.

• 95%+ accuracy rates• Production deployments• Real customer validation

ML via SQL-like queries - 12 ML features • Production-ready architecture

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:

Data Engineering
Feature Engineering
MLOps Platform
Key Limitations:
3-12 months timeline per use case
$500K-2M+ annual infrastructure costs
Requires specialized data science teams

Large Language Models

Strengths:

Natural language understanding and generation
Complex reasoning and creative tasks
Conversational interfaces
Automation Challenge:
Unreliable confidence metrics prevent automation
Non-deterministic behavior breaks consistent workflows
While LLMs excel at reasoning and explanation, only predictive databases provide the reliable confidence metrics essential for business automation

Aito Predictive Database

Architecture:

Upload Data
Query Predictions
Integrate API Responses
Key Advantages:
Hours to days implementation time
96%+ confidence scores enable automation
Processes millions of records for complete context

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

Complex system requiring ML expertise and ongoing maintenance

LLM Approach

Send invoice text to LLM with few-shot examples

Inconsistent confidence scores prevent automation - requires human review for every invoice

Aito Approach

Upload invoice data → Query for prediction with confidence score

96% confidence enables automation, automatic Bob→employee matching, adapts to new data

The Strategic Hybrid Approach

Most enterprises benefit from combining approaches strategically

Rapid deployment for 80% of enterprise AI use cases
Reliable automation with intelligent escalation
Cost-effective scaling without MLOps complexity

Enterprise Performance Metrics

Production-proven performance across enterprise deployments

20-168ms
Response Time Range
10M+
Records at Scale
100%
Test Reliability

Performance Benchmarks & Production Validation

Key metrics from production deployments and third-party testing

Key Performance Metrics

Response time range (100K to 10M+ records)20-168msSub-200ms maintained at enterprise scale
Average prediction accuracy96%Competitive with traditional ML models
Production accuracy (Posti invoice processing)95%+3,000+ invoices/month in SAP integration
Implementation timeHoursvs 3-12 months for traditional ML

Production Validation

Real customer deployments in production environments

Posti (Logistics)3,000+ invoices/month95% accuracy requirement consistently met
GridPane (Technology)Cloud infrastructure2-hour implementation time

Why Engineering Teams Choose Aito

Eliminate MLOps Complexity

No pipelines, no model deployments, no infrastructure to maintain. Just query predictions like data.

Replace traditional ML infrastructure with simple HTTP requests for prediction and classification use cases

Multi-Purpose Intelligence

One system handles predictions, recommendations, search, and analytics for structured data scenarios.

Single database serves structured data AI use cases through unified query interface

Developer Productivity

Existing development team can implement AI features. No specialized ML expertise required.

SQL-like syntax with comprehensive API documentation and SDK support

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

Available on AWS, Azure, GCP in customer accounts or managed EU (Ireland) hosting

Data Sovereignty

Complete control over data location and access with no vendor lock-in

Customer data never leaves your designated environment with full backup/export capabilities

GDPR Compliance

Built-in compliance features for European data protection requirements

Data processing agreements, right to deletion, consent management, and audit trails

Total Cost of Ownership Analysis

Estimated cost comparison for enterprise prediction use cases

Traditional ML

$500K-800K Data Science Team
$200K-400K MLOps Infrastructure
$200K-500K per project
$1M-2M+
3-12 months per model

Aito Approach

Usage-based pricing
$20K-50K integration
No additional infrastructure
No specialized team
Significantly lower
Hours to days

Cost Savings Breakdown

Team Costs$500K-800K/year$0 (existing developers)
Infrastructure$200K-400K/year$0 (no MLOps required)
Time to Market3-12 monthsHours to days

New integration! Aito Instant Predictions app is now available from Airtable Marketplace.