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Cake for
Data Extraction

Build scalable, composable pipelines to extract, clean, and prepare data from documents, APIs, and databases, optimized for agentic workflows and retrieval-augmented generation (RAG).

 

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Overview

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

  • Accelerated extraction across formats: Cake supported structured, semi-structured, and unstructured sources, including PDFs, images, and emails, using OCR, LLMs, and pre-built parsing pipelines.

  • Reduced manual intervention: By automating classification, labeling, and formatting, Cake eliminated the need for hand-tuned extraction rules or brittle regex-based workflows.

  • Improved accuracy with model supervision: Teams were able to fine-tune and supervise extraction models with human feedback, driving higher-quality results and more reliable downstream performance.

  • Integrated seamlessly with your stack: Data flowed directly into your lakehouse, warehouse, or vector database via prebuilt connectors and orchestrated pipelines.

  • Simplified compliance and traceability: Cake ensured sensitive data handling remained auditable and policy-aligned, with full lineage tracking across extraction workflows.

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Increase in
MLOps productivity

 

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Faster model deployment
to production

 

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Annual savings per
LLM project

THE CAKE DIFFERENCE

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From brittle scripts to reliable,
scalable data extraction

 

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Fragile scraping & OCR

Brittle pipelines that break on real-world data: Legacy tools and one-off scripts often fail when formats change or edge cases appear.

  • Requires heavy upfront parsing logic and manual tuning
  • Fails silently when document structure shifts
  • Poor accuracy on semi-structured or noisy documents
  • No observability, no built-in QA or evals

Result:

High error rates, wasted effort, and low confidence in output

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Data extraction with Cake

Production-grade extraction across any data format: Cake makes it easy to extract structured data from messy PDFs, HTML, chat logs, and more.

  • Pre-integrated tools for OCR, layout parsing, and schema normalization
  • Supports structured, semi-structured, and unstructured content
  • Built-in observability, retries, and quality evaluation
  • Easily plug in human review or post-processing where needed

Result:

Accurate, reliable pipelines that scale with your data

EXAMPLE USE CASES

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Where teams are using Cake to implement
advanced data extraction functionality

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RAG pipeline bootstrapping

Quickly ingest and chunk internal documents, web content, images, Word files, or PDFs to power RAG.

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Agentic workflow memory

Extract structured information from external sources to feed into multi-step reasoning agents. Integrate with vector databases like pgvector to provide agents with persistent, contextual memory.

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Document analytics

Parse and transform documents for downstream tasks like summarization, classification, or search indexing.

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Streaming change data capture

Continuously extract inserts, updates, and deletes from transactional databases using open-source CDC tools.

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Form and document parsing

Use AI-powered extractors to pull structured data from unstructured formats like PDFs, scanned forms, and images.

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Cloud API integration

Extract data from third-party platforms like Salesforce, Shopify, and Stripe through authenticated, rate-limited API workflows.

INGESTION & ETL

Stop wasting time on broken pipelines

See how Cake simplifies ingestion and ETL with a secure, production-ready stack, so your team can spend less time debugging and more time building.

Read More >

INTELLIGENT DOCUMENT PROCESSING

Tame document chaos with AI

Extract structured insights from PDFs, emails, and more (without the manual grunt work). See how Cake brings open-source IDP tools into one streamlined platform.

Read More >

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"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

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Scott Stafford
Chief Enterprise Architect at Ping

Read The Case Study
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"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

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"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

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Felix Baldauf-Lenschen
CEO and Founder

COMPONENTS

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Tools that power Cake's

data extraction stack

 

Frequently asked questions

What is data extraction in AI?

Data extraction in AI refers to the process of automatically pulling structured information from unstructured sources like PDFs, emails, images, and scanned documents using tools like OCR, LLMs, and pattern recognition algorithms.

How does Cake simplify data extraction workflows?

Can Cake handle different file types like images, PDFs, and emails?

Is Cake secure and compliant for regulated industries?

How is data extraction different from intelligent document processing (IDP)?

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