Free Webinar
Why Text Extraction Is NotDocument Intelligence
Most AI document workflows fail before the model ever runs. Tables flatten. Handwriting disappears. Reading order breaks. References lose their source. In this 45-minute webinar, we’ll show why text extraction is not enough, and what production AI actually needs from documents.
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For teams that need more than text extraction
This webinar is for teams already using OCR, parsing tools, or AI extraction, but still running into broken structure, inconsistent outputs, manual review, and documents that do not behave like clean inputs.
AI Builders
You’re building agents, copilots, or retrieval workflows that depend on documents.
You’ll get value from this session if you need to:
- Preserve tables, layouts, handwriting, and image-based data
- Improve retrieval quality before outputs reach the model
- Build AI workflows that can use documents as structured context
Platform & Data teams
You’re responsible for turning messy documents into something AI-ready.
You’ll get value from this session if you need to:
- Ingest complex documents without building custom parsers for every format
- Keep structure intact across PDFs, scans, tables, images, and attachments
- Produce measurable outputs before they move into production systems
Operations & Risk Leaders
You need document automation that can support real decisions.
You’ll get value from this session if you need to:
- Understand which outputs are reliable enough to automate
- Route low-confidence fields to review before they move downstream
- Reduce manual checking without removing human oversight where it matters
What We Will Discuss
We’ll walk through the failure patterns teams hit when they treat documents as flat text, then show what changes when documents are processed with structure, provenance, and confidence from the start.
Why AI keeps getting documents wrong
Most tools read documents as text streams. That breaks quickly when the document contains layout, tables, handwriting, images, captions, footnotes, cross-references, or multi-column sections. We’ll cover where these failures show up:
- Tables flattened into paragraphs. The numbers are still there, but the row, column, header, and cell relationships are gone.
- Handwriting and annotations missed. Typed text gets extracted, but the note that changes the interpretation disappears.
- Reading order destroyed. Multi-column pages, headers, sidebars, and footnotes get merged in ways no human would read them.
What document understanding actually means
Document Intelligence starts before generation. It turns the document into structured, traceable context before an agent or workflow uses it.
- Layout detection. The system identifies headers, paragraphs, tables, captions, footnotes, images, and page structure.
- Table preservation. Tables stay tables, so structured values can be queried, validated, and reused.
- Image classification. Charts, scanned tables, diagrams, and photographs are routed through different processing paths.
- Bounding-box provenance. Every extracted value traces back to the exact page and region it came from.
- Confidence scoring. Outputs are measured before they move downstream. High-confidence results can continue. Low-confidence fields can be reviewed, retried, or escalated.
Use Cases
We’ll Look Into Real Use Cases. Bring Yours
We’ll look at four common document-heavy workflows where plain extraction creates real downstream problems.
Safety-critical chemical data
A global manufacturer receives certificates of analysis in hundreds of vendor-specific formats. A single wrong digit on a flashpoint value creates legal liability.
Text extraction gives you strings. You need structured, normalized data with confidence scores on every value.
Specification ingestion at scale
A national general contractor needs to ingest 1,600-page spec books and automatically generate inspection checklists. The specs reference other documents and regulations.
Running the same process twice produced different results. You cannot build downstream automation on inconsistent outputs.
Dense document processing
Processing carrier statements, COIs, and plan documents with mixed handwriting and typed text. PDF-to-JSON libraries miss the structure entirely.
Dense 6-page statements have columnar layouts, nested tables, and mixed content. Text extraction loses all of it.
Product catalog intelligence
Sales staff need to find products matching technical specifications across thousands of marketing documents with inconsistent layouts.
Text extraction gives you marketing paragraphs. You need structured product attributes that can be queried and filtered.
Meet the Speakers
Kevin McGrath
Co-Founder & CEOKevin McGrath is the Co-Founder and CEO of Meibel, bringing over 20 years of experience in cloud infrastructure and platform engineering. He previously served as Vice President and General Manager at Spot by NetApp (2022-2024), where he led a global organization of over 1,000 people, and held roles as Chief Technology Officer and VP of Architecture at Spot (2017-2022). He holds AWS Certified Solutions Architect (Professional), AWS Certified DevOps Engineer (Professional), and earned his BA in Economics and Master's in Computer/Information Technology Administration from University of Maryland.
Aaron Aguillard
Head of Strategic GrowthAaron Aguillard leads Strategic Growth at Meibel, building enterprise partnerships and scaling go-to-market strategy. He brings over 15 years of experience scaling revenue and building strategic alliances in AI, SaaS, and cybersecurity. Prior to Meibel, Aaron served as Founding CRO at Qualifire (2024-2025), an AI security startup where he secured partnerships with TCS and Google Cloud and built the GTM foundation from pre-launch to enterprise traction. Before that, he spent four years as Director of Channel Sales at Namogoo (2020-2024), where he built and led global strategic partnerships with global brands including Infosys, TCS, Deloitte, and BCG.
Frequently Asked Questions
Who is this webinar for?
This webinar is for AI builders, platform teams, data teams, and operations leaders working with complex documents. If you’re using OCR, parsers, RAG, or AI extraction and still dealing with broken structure, manual review, or inconsistent outputs, this session is for you.
What will this webinar cover?
We’ll cover where text extraction breaks and what reliable document workflows need before the model runs: layout detection, reading order, table preservation, image classification, provenance, and confidence scoring.
How is Document Intelligence different from OCR?
OCR reads characters. Document Intelligence keeps the document usable. It preserves layout, tables, handwriting, images, captions, footnotes, source locations, and the relationships between elements.
Why does text extraction break on complex documents?
Text extraction treats a document like a stream of text. That breaks when the file has tables, multi-column layouts, scanned pages, charts, handwritten notes, or references to other documents. The content comes through, but the structure disappears.
Will there be a live demo?
Yes. We’ll run a complex document through the workflow and show parsed structure, structured extraction, confidence scores, source provenance, and the difference between basic text extraction and Document Intelligence.
Will the webinar be recorded?
Yes. Register for the session, and you’ll receive the recording after the webinar, even if you can’t attend live.
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