Cake for Data Extraction

Extract smarter with open-source, cloud-agnostic tooling

LLMs are only as useful as the data they can access. Most organizations have valuable information locked away in PDFs, webpages, databases, or siloed APIs. To enable advanced use cases like RAG or agentic workflows, teams need robust extraction pipelines that can reliably handle unstructured data at scale.

Cake provides a modular framework that connects file readers, document parsers, ingestion pipelines, and vector stores. Whether you want to extract data from PDFs, Word files, or images, pull data from third-party APIs, or query vector databases like Weaviate, Cake provides pre-built connectors and orchestration tools that work.

Unlike brittle one-off scripts or rigid proprietary solutions, Cake’s data extraction stack is built from open-source components and designed to be cloud agnostic, swappable, and compliant with regulations like SOC2 and HIPAA. It's designed to integrate cleanly with LLM orchestration layers like Langflow or LlamaIndex.

Key benefits

• Speed up RAG and agentic development: Go from raw files to chunked, indexed context faster, using modular, pre-tested components.
  
  
• Stay flexible and modular: Swap ingestion, transformation, or storage tools freely, reroute pipelines, and avoid vendor lock-in.
  
  
• Build with security in mind: Enterprise-ready security, lineage, and auditability from the start. Compliance with industry standards like SOC2 and HIPAA.