Lotto Forecaster AI

Lottery analysis becomes unreliable when unrelated draw histories are mixed, imported data is not validated, model versions are not traceable or probabilistic forecasts are presented as guarantees. The opportunity is to provide a disciplined, reproducible analytical system with isolated learning contexts and auditable release artifacts.

• Keep each supported lottery type in an isolated learning environment.
• Automate ingestion and validation of new official draw data.
• Stage CSV/OCR data before committing it to the production dataset.
• Train, evaluate and retrain forecasting models under controlled gates.
• Provide ranked candidate forecasts with confidence and supporting reasoning.
• Support secure accounts, wallets, subscriptions and Paystack billing.
• Maintain traceable Android release metadata and binary integrity.

• Prevent cross-contamination between lottery-type datasets and models.
• Validate historical draw data before production commit.
• Present probabilistic forecasts responsibly without implying guaranteed outcomes.
• Coordinate mobile app, backend, model artifacts and release metadata across versions.
• Support automated retraining while preserving traceability and evaluation gates.

The platform combines a Flutter/Dart Android client with a Python FastAPI service and Supabase/PostgreSQL. Data import workflows stage CSV and OCR results before commit. Each lottery type has isolated analysis/training context. Model artifacts and evaluation records are versioned, and the mobile client consumes authenticated forecast and account services.

• Advanced historical draw analysis
• AI-assisted pattern recognition
• Lottery-type-specific forecasting models
• Candidate number scoring and ranking
• Confidence scoring
• Machine learning model training
• Automatic retraining after new draw data
• Pattern drift detection
• Ensemble forecasting
• Historical replay and backtesting
• Automated draw-data retrieval
• Lottery-type data isolation
• Flutter-based mobile application
• Cloud-connected data architecture
• Secure wallet and subscription access
• Paystack payments

• Authenticated application and import endpoints.
• Supabase/PostgreSQL row-level security architecture.
• Server-controlled wallet and subscription logic.
• Lottery-type data isolation.
• Versioned model artifacts and training manifests.
• Signed Android production release with verified SHA-256 integrity.

The backend separates mobile presentation, API services, persistent data and model execution. Supabase/PostgreSQL handles durable application data while containerized FastAPI services support independent backend scaling. Model retraining and data ingestion are separated from the interactive mobile UI.

The Android client organizes draw import, analysis, forecasts, saved predictions, account state and subscription access into a mobile-first workflow. Forecast output is structured to show ranked candidates and confidence rather than unqualified claims.

Live production application

Forecasting systems need stronger data discipline than ordinary CRUD applications. Dataset isolation, staging, versioned artifacts, evaluation gates and release integrity are essential when model outputs depend on historical records and continuous retraining.