StreamForge AI is a real-time data platform designed for ML/AI workloads, integrating CDC ingestion, stream processing, feature storage, and serving. The architecture comprises distinct layers: ingestion (Debezium/Kafka), processing (Flink), storage (MinIO/S3 with optional Iceberg), and serving (online/offline feature store). Key components include a control plane API/UI for pipeline management, a Feast/Tecton-compatible feature store, schema registry client, drift detector, lineage tracker, and prefetch engine for ML training optimization. The platform supports end-to-end pipelines from database changes to feature materialization and inference, with examples demonstrating ML training, real-time serving, and feature consumption. While the system demonstrates strong modularity and integration of industry-standard tools, its operational complexity and polyglot nature (Java/Python/JS) present maintenance challenges.