Fleak

Fleak AI Workflows: Real-time AI & Enrichment with a Serverless API Builder

Fleak is a low-code, serverless platform designed for data teams to instantly build, deploy, and scale APIs and AI-powered workflows. It seamlessly connects to your existing AI and data stack, removing infrastructure management and enabling rapid iteration from data to production APIs.

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Overview

• Build data workflows quickly using a visual, node-based editor that supports JSON, SQL, CSV, and plain text data.
• Integrate with leading AI models, databases, and storage services (e.g., GPT-family models, LLMs, vector databases, AWS Lambda, Pinecone, AWS S3, Snowflake).
• Publish, manage, and monitor APIs with versioning and production-grade endpoints.
• Serverless architecture minimizes operational overhead, enabling focus on logic and insights rather than infrastructure.

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How Fleak Works

1. Create and configure workflow nodes. Define data transformations, embeddings generation, and data routing using a low-code interface.
2. Connect AI models and data stores. Integrate large language models, vector databases, storage, and other essential tools to orchestrate AI-enabled data workflows.
3. Transform and enrich data. Process data types like JSON, SQL, CSV, and plain text; generate text embeddings; and route results to storage or downstream services.
4. Publish and monitor APIs. Version workflows, push to staging/production, and monitor performance and data accuracy from a single platform.

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Use Cases

• Building production-ready AI data pipelines and APIs in minutes
• Real-time data enrichment and orchestration across LLMs and vector stores
• Data-to-API workflows that scale with minimal infrastructure management
• Rapid experimentation with different AI models and storage backends

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How It Helps Teams

• Data scientists, data engineers, and software engineers can collaborate on AI-driven workflows without heavy DevOps.
• Centralized management for API endpoints, versioning, and monitoring to reduce time-to-value.
• Storage-agnostic design for flexible integration with cloud data warehouses or lakehouses.

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Core Capabilities

• Low-code, serverless platform for building and deploying AI-enabled APIs
• Visual workflow editor supporting JSON, SQL, CSV, and Plain Text data
• Seamless integration with AI models (GPT, LLaMA, Mistral, etc.) and LLM orchestration
• Connections to vector databases, AWS Lambda, Pinecone, and modern storage (AWS S3, Snowflake, etc.)
• In-memory SQL and LLM nodes for low-latency processing
• Publish, version, and monitor APIs from a single interface
• Production-ready deployment with HTTP endpoints
• Storage-agnostic design for flexible data storage choices

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How to Use Fleak

1. Start by creating a new workflow and add nodes for data intake and transformation.
2. Configure nodes to call AI models, generate embeddings, and interact with databases or vector stores.
3. Test the workflow, preview results, and refine as needed.
4. Version the workflow, push to staging or production, and expose APIs via HTTP endpoints.
5. Monitor performance and data accuracy from the built-in dashboards.

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Safety and Governance (Guidance)

• Use Fleak to build production-grade data pipelines with clear versioning and change management.
• Ensure appropriate access controls and data privacy practices when connecting to sensitive data sources.

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Example Workflows

• Real-time Slack history chatbot with embedded data from vector stores
• Product recommendations tailored to user data and external signals
• RAG-enabled LLM responses using Pinecone for retrieval

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Pricing & Access (from the provided context)

• Try Free and Request a Demo options available
• Use Cases, Templates, and a catalog of integrations and partners

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About Fleak

A platform designed for data teams to collaborate on AI transformations over API endpoints, combining ease of use with scalable, production-ready deployments. It emphasizes low-code orchestration, serverless deployment, and broad integration capabilities to simplify complex AI workflows.