Fleak AI Workflows

What is Fleak AI Workflows?

Fleak AI Workflows is an AI-native data infrastructure layer that transforms raw, messy, and ungoverned data into a trusted, real-time signal for AI applications. It sits between your data sources and AI systems, automatically normalizing, filtering, and governing data based on downstream needs. The platform evaluates each incoming data point against application requirements, routing high-value data in real time, storing compliance-relevant data long-term, and discarding irrelevant data. This eliminates the need for manual pipeline maintenance and reduces integration time from months to weeks.

Key features include automatic schema drift detection and self-healing: when upstream data changes, Fleak detects the drift, generates a new configuration, and redeploys—often without human intervention. It enforces fine-grained access control at the data layer, logs every transformation, and provides a full audit trail, meeting SOC 2 Type II standards. The platform supports millions of events per second with zero storage required for real-time processing, and reduces storage costs by 50% through deduplication at ingestion. LLM token costs drop by 40% thanks to normalized, deduplicated inputs.

Fleak connects to any data source—cloud, OT, endpoints, APIs, databases—without custom connectors. Users describe desired workflows in natural language, and Fleak’s copilot builds them in minutes. It identifies event types, branches by destination intent, and normalizes to the correct schema. Clean, governed data is delivered to AI applications, data lakes, SIEM, or any destination. Use cases include real-time AI inference, fraud detection, customer 360, and operational analytics. Technical details: the platform uses a streaming architecture with sub-second latency, supports REST and WebSocket APIs, and integrates with major cloud providers. Fleak AI Workflows empowers data and AI teams to escape maintenance mode, control costs without sacrificing coverage, and ensure AI models reason over trusted, high-quality data.

Use Cases

Key Features

automatic schema drift detection self healing pipelines real time data routing fine grained access control full audit trail SOC 2 Type II compliance deduplication at ingestion LLM token cost reduction zero storage real time processing storage cost reduction

Who is it for?

AI engineers, data engineers, data platform teams, compliance officers, IT managers, DevOps teams, data governance teams

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