Fleak

Fleak is an AI-native infrastructure layer designed to bridge the gap between raw, unstructured data and production-grade AI applications. It functions as a middleware platform that ingests data from diverse sources—databases, APIs, streaming logs, data lakes, and SaaS tools—then automatically normalizes, filters, governs, and delivers that data in real time to any AI model, vector store, or retrieval system. The core problem Fleak solves is the degradation of AI output caused by inconsistent, noisy, or ungoverned input data. Instead of forcing data engineers to manually clean, transform, and maintain pipelines for every new source, Fleak provides a unified pipeline that applies schema inference, deduplication, anomaly detection, and access control at the ingestion layer. The platform operates as a data control plane between your data stores and your AI stack. It connects to sources like PostgreSQL, Snowflake, BigQuery, Kafka, S3, and REST APIs, automatically detecting schema changes and adapting the pipeline without engineering intervention. It applies lightweight transformations—normalizing date formats, stripping HTML, correcting typos, resolving entity references—before the data reaches the AI application. For governance, Fleak enforces field-level permissions, redacts PII, and logs all data lineage so that every input to a model is traceable back to its origin. On the delivery side, Fleak supports real-time streaming via WebSocket, server-sent events, and push-based APIs, as well as batch loading into vector databases like Pinecone, Weaviate, or Chroma. It can also output to traditional SQL databases or data warehouses for auditing. The platform is agnostic to the AI framework—whether you use LangChain, LlamaIndex, custom embeddings, or fine-tuned open-source model