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Tagline:Your AI is only as smart as your worst data source.

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What is Fleak?

Fleak is the AI-native data layer that turns messy, inconsistent, ungoverned data into a trusted signal for every AI application. It evaluates incoming data against downstream needs, routes value-aware pipelines, and self-heals when schemas drift.

How to use Fleak?

1. Connect any source — cloud, OT, endpoints, APIs, or databases. 2. Use natural language in Fleak's copilot to orchestrate transformations. 3. Let Fleak identify event types, normalize schemas, and update configs when data changes. 4. Deliver clean, governed data to AI apps, data lakes, SIEM, or other destinations in real time.

Fleak Core Features

Value-aware routing

Fleak's AI evaluates every incoming data point against what your downstream applications need, then routes real-time value fast, compliance value to long-term storage, and drops what has no value.

AI-orchestrated, self-healing pipelines

When schema changes upstream, Fleak detects it, generates a new config, and redeploys automatically. Human review is optional.

Governed delivery to any destination

Fine-grained access control is enforced at the data layer. Every transformation is logged with a full audit trail.

Brain and Muscle architecture

The Brain layer understands your data and generates transformation configs. The Muscle layer (Zephflow) executes them deterministically at scale with no AI overhead at runtime.

Fleak Use Cases

Normalize security telemetry to OCSF, UDM, or CSF before it reaches SIEM or detection tools

Unify OT and IoT telemetry into a consistent schema for analytics and predictive models

Reduce LLM token costs by feeding AI agents normalized, deduplicated event streams

Fleak Frequently Asked Questions

What is Fleak?

Fleak is a managed data workflow platform for building distributed data pipelines using a DAG builder UI, with support for source connectors, processing nodes, and multiple sink destinations.

How do I create and deploy a workflow?

Create a workflow in Fleak's DAG builder, test it with sample data, publish a version, deploy it to a compute cluster, and monitor its execution.

What happens when upstream schemas change?

Fleak detects schema drift, generates an updated transformation config in about three minutes, and surfaces it for review before anything downstream breaks.

Fleak Product Background & Official Links

Fleak Support Email & Customer service contact & Refund contact etc.

Fleak Support Email: contact@fleak.ai

More Contact, visit the about us page(https://docs.fleak.ai/workflowbuilder)

Fleak Pricing & Plans

Enterprise

contact

Book a demo

Value-aware data routing and governed delivery
AI-orchestrated self-healing pipelines
50+ source integrations
SOC 2 Type II with full audit trail

Subscribe & upgrade now

Fleak Tags

API Available Web App Cloud-based Real-time For Enterprise AI Agent Ready Secure Data No-Code

Best Suited Occupations for Fleak

Software Developers Data Scientists Product Managers

Fleak

Active

Tagline:Your AI is only as smart as your worst data source.

Updated:-

Social Media:

AI Data Analytics AI Workflow AI Developer Tools API Available Web App Cloud-based Real-time For Enterprise AI Agent Ready Secure Data No-Code

Open Website

Visit Website

Promote this product|Claim this product

What is Fleak?

How to use Fleak?

Fleak Core Features

Value-aware routing

Fleak's AI evaluates every incoming data point against what your downstream applications need, then routes real-time value fast, compliance value to long-term storage, and drops what has no value.

AI-orchestrated, self-healing pipelines

When schema changes upstream, Fleak detects it, generates a new config, and redeploys automatically. Human review is optional.

Governed delivery to any destination

Fine-grained access control is enforced at the data layer. Every transformation is logged with a full audit trail.

Brain and Muscle architecture

The Brain layer understands your data and generates transformation configs. The Muscle layer (Zephflow) executes them deterministically at scale with no AI overhead at runtime.

Fleak Use Cases

Normalize security telemetry to OCSF, UDM, or CSF before it reaches SIEM or detection tools

Unify OT and IoT telemetry into a consistent schema for analytics and predictive models

Reduce LLM token costs by feeding AI agents normalized, deduplicated event streams

Fleak Frequently Asked Questions

What is Fleak?

Fleak is a managed data workflow platform for building distributed data pipelines using a DAG builder UI, with support for source connectors, processing nodes, and multiple sink destinations.

How do I create and deploy a workflow?

Create a workflow in Fleak's DAG builder, test it with sample data, publish a version, deploy it to a compute cluster, and monitor its execution.

What happens when upstream schemas change?

Fleak detects schema drift, generates an updated transformation config in about three minutes, and surfaces it for review before anything downstream breaks.

Fleak Product Background & Official Links

Fleak Support Email & Customer service contact & Refund contact etc.

Fleak Support Email: contact@fleak.ai

More Contact, visit the about us page(https://docs.fleak.ai/workflowbuilder)

Fleak Pricing & Plans

Enterprise

contact

Book a demo

Value-aware data routing and governed delivery
AI-orchestrated self-healing pipelines
50+ source integrations
SOC 2 Type II with full audit trail

Subscribe & upgrade now

Fleak Tags

API Available Web App Cloud-based Real-time For Enterprise AI Agent Ready Secure Data No-Code

Best Suited Occupations for Fleak

Software Developers Data Scientists Product Managers