Fleak offers an AI‑native data fabric that automates the ingestion, schema translation, enrichment, and routing of complex data streams, delivering clean, AI‑ready output to any destination. Its in‑motion orchestrator continuously monitors pipelines, self‑heals from schema drift, and enforces governance, allowing data engineering teams to deploy and scale real‑time integrations in minutes without manual effort.
Funding
$5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
8OSSFounders
Product
Problem
Data teams spend months manually integrating, normalizing, and governing diverse data streams, leading to high engineering costs, delayed product launches, and fragile pipelines that risk compliance and revenue growth.
Solution
Fleak provides an AI‑native data fabric that automates ingestion, schema translation, enrichment, and routing of complex data streams. By embedding intelligence into the pipeline, Fleak eliminates manual “data janitorial” work, automatically adapts to schema changes, and ensures consistent, clean, AI‑ready output to any destination such as data lakes, warehouses, or vector stores. The platform operates as an in‑motion orchestrator, continuously monitoring and self‑healing pipelines to maintain data integrity and compliance. Users can deploy the solution in minutes and scale it to high‑volume, real‑time workloads without additional engineering effort. Fleak integrates with common data stack components (e.g., Snowflake, Amazon S3, AWS Lambda, Pinecone) via native connectors, enabling seamless data flow into existing analytics and AI environments.
Target Audience
Primary customers are data engineering and analytics teams at high‑growth enterprise SaaS, cybersecurity, and logistics platforms that need rapid, reliable integration of large, heterogeneous data sets for AI and analytics workloads.
Features
- AI‑driven ingestion engine that automatically discovers, connects to, and parses diverse data sources
- Schema intelligence (e.g., OCSF, CIM) that normalizes and enriches data in‑flight, handling schema drift without manual updates
- Self‑healing pipelines with automated error detection, notifications, and runtime recovery
- One‑click integration with major cloud and data‑platform services (Snowflake, S3, Lambda, Pinecone) and support for custom destinations
- Real‑time data routing and transformation that delivers AI‑ready output to lakehouse, vector databases, or downstream analytics
- Centralized governance layer that enforces data consistency, compliance, and auditability across all streams