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AI for PDFs

AI for PDFs, liberate yourself from PDF text extraction hell

Building robust AI pipelines on PDF documents is a complex and often unrewarding task. PDFs are a protected format that defy simple text extraction, resulting in low-quality data that causes RAG agents to fail and hallucinate.

BookWyrm handles complex and large PDFs at scale, transforming them into clean, AI-ready data your business can trust.

The ProblemComplex PDF Example
AI for PDFs diagram

The Problem: PDFs Break RAG

Developers know the pain of processing PDFs. It's not just about getting text out, it's about getting high-quality, context-preserving text out at scale. PDFs can result in RAG and AI Agent failure, due to:

Low-Quality Extraction

Unlike simple files, extracting a PDF's inner text requires computationally-intensive processes like OCR or IDP. Open-source tools like Tesseract struggle with scale, leading to jumbled text, broken context, and silent failures when processing thousands of documents.

Polluted Chunks

  • Line numbers, headers, and footers pollute your chunks.
  • Noise removal is brittle, requiring custom, document-specific scripts that take days to write and break constantly.

Lost Semantics

  • Fixed-size splits cut sentences in half, wrecking semantic meaning.
  • Indexing bloats with junk text, making retrieval noisy and unreliable.

Lack of Trust

  • Agents trained on messy data hallucinate and lack traceability, leading to a loss of business trust in your outputs.

Agents hallucinate, pipelines fail silently, and you lose trust in your outputs.

The Fix: Production-ready endpoints that actually work

BookWyrm abstracts away the computational complexity and data-cleaning headache of PDF processing, providing production-ready endpoints that integrate seamlessly into your pipeline.

We handle the IDP/OCR complexity, so you don't have to build proprietary tooling or manage open-source scaling issues.

With just a few lines of code, BookWyrm lets you:

Extract from PDF (high-fidelity):

Extract clean, high-quality text from native or image PDFs, including positional information (page, location) showing exactly where the text came from.

Noise removal & clean-up:

Automatically strip out headers, footnotes, and irrelevant formatting that pollutes your vector index.

Phrasal chunking:

Split text into semantically meaningful chunks, not arbitrary token splits, to preserve context and boost retrieval accuracy.

Classify PDFs:

Instantly know what type of document you're handling (e.g., invoice, report, legal) and route it correctly for downstream processing.

Summarization:

Collapse long PDFs into concise, usable overviews for efficient context injection.

Citation under question:

Ground answers with traceable sources and reasoning, ensuring your RAG output is trustworthy and accountable.

Learn more about multimodal extraction and outputting JSON from your documents.

Complex PDF Example

For enterprise clients, we offer the ability to handle poor quality scanned PDFs. The below example is taken from Heinrich Palaces, a scanned German book.

Example of a complex PDF being processed by BookWyrm

We used this command:

bookwyrm extract-pdf data/Heinrich_palaces.pdf --num-pages 1 --start-page 18

BookWyrm Output

Even from a poor-quality scanned PDF, BookWyrm outputs structured, AI-ready data with text blocks, confidence scores, and coordinates, allowing you to reconstruct the correct reading order, index content that would otherwise stay hidden, and surface it for real business use. Perfect for back office automation. These types of uses can also be applied to other document types too.

{
  "pages": [
    {
      "page_number": 18,
      "text_blocks": [
        ...
        {
          "text": "Taf. 15 im arabischen Teil); der Raum ist wahrscheinlich ein Bad. Auch das spricht dafür, da Raum 4 überdeckt",
          "confidence": 0.9871127605438232,
          "bbox": [
            [
              117.0,
              87.0
            ],
            [
              1349.0,
              129.0
            ],
            [
              1347.0,
              162.0
            ],
            [
              116.0,
              120.0
            ]
          ],
          "coordinates": {
            "x1": 116.0,
            "y1": 87.0,
            "x2": 1349.0,
            "y2": 162.0
          }
        },
        ...
      ]
    }
  ]

Your life without ever having to extract text from PDFs again

Faster RAG prototypes that actually work

Instead of weeks building brittle, custom preprocessing scripts and managing the computational scale of IDP/OCR, you drop in BookWyrm's endpoints and get production-ready data in minutes.

Reliable agents your business teams can trust

By eliminating text pollution and ensuring semantic chunking, your agents work with clean, traceable data, drastically reducing hallucination rates.

More time spent building features, less time cleaning PDFs

Shift your engineering focus from maintenance and data wrangling to developing core product features and value-add workflows.

Built for Scale

Handle thousands of documents without worrying about computational overhead, distribution, or network limits. Our API scales instantly with your demand.

BookWyrm Delivers Your Agentic Workflows Strategy.

Your data pipeline is the foundation for your agentic workflows. Build it right. Get started with the API that's fast to set up, easy to extend, and built for developers.

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