How our AI works
No black boxes. We believe you deserve to know exactly how your data is analyzed, what models are used, and how confident we are in every insight.
Our Approach
We don't rely on a single algorithm. LottoLabs uses an ensemble approach that combines classical statistical methods with modern machine learning. Each model sees your data from a different angle — and the ensemble combines their strengths while canceling out individual weaknesses.
Ingest & Clean
Your data is validated, normalized, and missing values are handled automatically.
Feature Engineering
Statistical features, lag variables, and domain-specific transformations are generated.
Multi-Model Analysis
Multiple models run in parallel — each specialized for different pattern types.
Ensemble & Validate
Results are combined, cross-validated, and ranked by confidence score.
Models We Use
Each model is chosen for a specific job. Here's what runs under the hood.
Time-Series Analysis
Auto-Regressive Integrated Moving Average for stationary series and short-term trend analysis
Meta's decomposable model that handles seasonality, holidays, and trend changepoints
Long Short-Term Memory networks for capturing complex non-linear temporal dependencies
Clustering & Segmentation
Fast centroid-based clustering for well-separated groups with automatic K selection via silhouette analysis
Density-based clustering that discovers arbitrarily-shaped clusters and identifies noise points
Anomaly Detection
Tree-based algorithm that isolates anomalies by random partitioning — efficient on high-dimensional data
Statistical method to flag data points that deviate significantly from the distribution mean
Pattern Recognition
Our proprietary transformer-based architecture, fine-tuned for tabular and sequential pattern discovery
Full Transparency
Every insight comes with a confidence score. Every anomaly flag explains why it was flagged. Every cluster shows you the features that defined it. We don't hide behind “the AI said so.”
- Confidence scores (0–100%) on every insight
- Feature importance rankings for each result
- Plain-language explanations alongside statistical output
- Full audit trail of model decisions
AI Summary: Strong upward trend detected with 94% confidence. Seasonal component repeats every 7 days. One anomaly flagged at row 847 — value is 3.2 standard deviations above the mean.
Rigorous Backtesting
Before any model is used on your data, it's validated against historical data using walk-forward cross-validation. We hold out recent periods as test sets, and only models that pass our accuracy thresholds are used in production.
- Walk-forward cross-validation on every dataset
- Minimum accuracy threshold before deployment
- Automatic model re-training when performance degrades
- Full backtest reports available for download
Accuracy Metrics
Real numbers from real benchmarks. We're honest about what our AI can and can't do.
Measured across 10K+ real-world datasets
Balanced precision and recall on labeled benchmarks
Average across diverse dataset types
Known pattern recovery rate in controlled tests
Honest disclosure: Accuracy varies by dataset. These are aggregate benchmarks. Highly irregular or sparse data may see lower accuracy. We always show you the confidence interval so you can decide.
Data Privacy
Your data belongs to you. Period. We built our infrastructure with privacy as a foundation, not an afterthought.
- AES-256 encryption at rest, TLS 1.3 in transit
- Your data is never sold, shared, or used to train our models
- Complete data deletion on request within 24 hours
- Isolated tenant environments — zero cross-contamination
- GDPR and CCPA compliant by design
Research Foundation
Our methodologies are grounded in peer-reviewed research and battle-tested statistical frameworks.
Statistical Foundations
Box-Jenkins methodology, Bayesian inference, and robust estimation techniques form our statistical backbone.
Machine Learning
Ensemble methods (Random Forest, Gradient Boosting), neural networks (LSTM, Transformer), and kernel methods.
Validation Frameworks
Walk-forward validation, k-fold cross-validation, and out-of-sample testing ensure generalization.
Reproducibility
Every analysis is versioned and reproducible. Same data in, same results out — every time.
Continuous Improvement
Our models are updated quarterly based on the latest research and performance feedback.
Open Standards
We use open data formats and publish our evaluation benchmarks for community review.
See the AI in action
Upload your first dataset and watch our AI find patterns you didn't know existed. Free tier available — no credit card required.