Project overview
Intended market
Adult users seeking structured, data-driven analysis of Ghana NLA lottery history, with clear responsible-use framing that forecasts are analytical estimates and not guarantees of winning.
Engineering responsibility
Flutter Android application development; FastAPI backend engineering; Supabase/PostgreSQL data architecture; authenticated import and OCR workflows; model-training and evaluation pipelines; lottery-type data isolation; release engineering; wallet, subscription and Paystack integration.
Problem or opportunity
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.
Objectives
- 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.
Challenges and constraints
- 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.
Engineering approach
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.
Architecture
Architecture narrative
Flutter provides the Android client for import, analysis, forecast and account workflows. FastAPI provides authenticated application and model-service endpoints. Supabase/PostgreSQL stores draw history, account data and application state. Supporting Edge Functions handle selected cloud tasks. Production backend services are containerized and deployed through cloud infrastructure.
frontend
Flutter + Dart Android application
backend
Python + FastAPI
database
Supabase + PostgreSQL
infrastructure
Vercel and Google Cloud Run deployment components
services
Supabase Edge Functions, OCR, machine-learning model pipeline
integrations
Paystack, Twilio Verify, automated draw ingestion
Engineering decisions
Isolate every lottery type
Reason: Different draw histories must not contaminate one another.
Engineering benefit: Training and evaluation remain lottery-specific and reproducible.
Stage imported data before commit
Reason: CSV and OCR sources can contain errors or duplicates.
Engineering benefit: Potentially invalid data can be reviewed before mutating the authoritative dataset.
Version model artifacts and release metadata
Reason: Forecast behavior and Android binaries need traceability across updates.
Engineering benefit: Training runs and production releases can be independently verified.
Use server-controlled billing and forecast access
Reason: Wallet/subscription state must not be trusted to the mobile client alone.
Engineering benefit: Reduces tampering risk around paid forecast access.
Core capabilities
- 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
Challenge, response, outcome
Unrelated lottery histories can distort models.
Response: Maintain isolated datasets, training contexts and evaluation records for each lottery type.
Verified outcome: Forecast generation remains specific to the selected NLA game.
Imported historical records may contain OCR or CSV errors.
Response: Stage and validate records before commit to the authoritative dataset.
Verified outcome: Data mutations are controlled and reviewable.
Forecasts can be misinterpreted as guarantees.
Response: Present ranked analytical estimates with confidence information and responsible-use language.
Verified outcome: The product avoids claiming guaranteed winning outcomes.
Security and reliability
- 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.
Performance and scalability
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.
User experience
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.
Technology architecture
cloud
Supabase Edge Functions · Vercel · Google Cloud Run · Docker
mobile
Flutter · Dart
backend
FastAPI · Python
database
Supabase · PostgreSQL
analytics_and_ml
Machine Learning · NumPy · OCR
payments_and_identity
Paystack · Twilio Verify
Approved project evidence
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Verified results and supporting evidence
Current status
Live production application
Engineering insights
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.
Acquire this application or request a similar build
This is a Frontier-owned product. You can open a commercial acquisition discussion for the existing application, or request a separate solution built around similar engineering capabilities.