Realtime Decision Platform

AI-driven real-time decision platform. An event-driven, multi-tenant architecture for high-frequency data streams with predictive intelligence and automated execution.

  flowchart LR
    A[Event sources] --> B[Ingest daemon]
    B --> C[Decision engine]
    C --> D{"Risk score?"}
    D -->|High| E[Action out]
    D -->|Low| F[Pass]
    C --> G[(Audit log)]

What the platform delivers

A production-grade real-time pipeline that turns raw data streams into convergent decisions — from stream ingest to validated action.

• 📡 Stream ingest — WebSocket capture from three primary and two enrichment data sources, normalized, validated, persisted.
• 🧠 Predictive intelligence — XGBoost models with ONNX inference classify regimes across three dimensions: direction, volatility, quality. No speculative point forecasts.
• ⚡ Event-driven — PostgreSQL LISTEN/NOTIFY with durable events. At-least-once guarantees, cursor-tracked replay after reconnects.
• 👥 Multi-tenant — Per-tenant keys, limits, kill-switch, risk profiles. Stored procedures + row-level security as the hard boundary.
• 🛡️ Production-ready — Circuit breaker per data source, health monitoring per component, feature validator with DATAMISSING auto-recovery, email/XMPP alerting.
• 🔄 Self-optimizing — Optuna-based hyperparameter tuning with walk-forward holdout, champion/challenger waves, outcome feedback loop, and quality gates.

Architecture principles

Battle-tested choices that guarantee scaling, maintainability, and auditability.

• 🔁 Async-first — All I/O operations are asynchronous. 51 % of all functions are async def. Thousands of parallel streams without thread overhead.
• 📐 Config-driven — Data source mappings, fallback chains, unit conversions, and thresholds live entirely in JSON. No hardcoded magic numbers.
• ✅ Data quality first — Single source of truth for every metric. Validators before persistence, validators before model training, health monitoring at runtime.
• 🚀 Vectorized — NumPy bulk operations for training and replay simulation. No Python loops on the data hot path.
• 🧪 Simulator parity — Live path and simulator share the same code for every critical decision. Replay results mirror production.
• 📊 Observability — 21 live dashboards, structured logging across console, file, and database. Heartbeats, latencies, drift detection.

Technology foundation

Mature open-source components — production-proven, royalty-free, auditable.

Quantitative metrics

Current project scope after 10 fully completed development phases.

Market value

Conventional effort estimation per COCOMO II, including domain complexity factors.

Productivity multiplier compared to conventional solo development: 8–12×. Methodology documented and reproducible.

Realtime Decision Platform — 10 phases · 7/7 daemons · 100k SLOC · production ML/AI pipeline