I’m building a VLM-based document intelligence pipeline for complex PDFs.
One thing I’m experimenting with heavily is Cloudflare R2 for document storage.
The interesting challenge isn’t just the model — it’s figuring out how to build a pipeline that can reliably handle real-world documents at scale.
Still building. Still breaking things. 😅
More to come.
Most document pipelines start by extracting text and then trying to reconstruct the document’s meaning.
I’m taking a different approach: using a pure Transformer-based Vision-Language Model to understand the entire page — text, tables, charts, diagrams, layout, and visual relationships — while keeping everything traceable back to the original page.
Still building it. What would you change or challenge about this approach?
I’m building a document intelligence system that uses Vision-Language Models to process visually complex documents.
A recurring limitation I’ve noticed with conventional document pipelines is that text extraction alone often loses important context. Tables, charts, diagrams, scanned pages,spatial relationships, and visual hierarchy may carry as much meaning as the text itself.
The approach I’m exploring treats each page as a visual document rather than only a collection of extracted text. The current pipeline focuses on:
- Understanding text, layout, tables, charts, images, and diagrams
- Processing scanned and digitally generated PDFs
- Preserving page-level provenance for citations
- Supporting semantic search across document collections
- Extracting structured information without discarding visual context
- Allowing users to verify results against the original page
Before deciding what to prioritize, I’d like to learn from people who already work with document automation.
What conditions would need to be met before you could use a document intelligence platform with real business documents?
I’m still actively building, so honest technical criticism and real-world workflow examples would be extremely helpful. If anyone is interested in testing an early version,
Feel free to comment and share your thoughts
Hi everyone — I’m new to Reddit and still learning how different communities work, so please let me know if there’s a better way to share this or ask for feedback here.
I’m building a document intelligence SaaS for people and teams working with complex PDFs, scanned reports, contracts, research papers, manuals, and similar documents.
Rather than relying only on OCR or extracted text, it uses Vision-Language Models to understand the complete page—including text, layout, tables, charts, images, diagrams,and the relationships between them.
The current direction includes:
- Structured information extraction
- Questions across document collections
- Page-level citations and source verification
- Semantic document search
- Table, chart, image, and diagram understanding
- Custom projects and document organization
- APIs for integration with existing workflows
It’s being offered as a SaaS initially. I’m planning an on-premises version later for organizations with stricter privacy, security, compliance, or data-residency requirements.
Before deciding what to prioritize, I’d like to understand what potential users actually expect from this kind of platform.
A few questions:
- What document types do you regularly process?
- Which features would be essential for your workflow?
- Where do existing document AI tools fall short?
- What accuracy or verification standards would you require?
- Would you need custom extraction schemas?
- Are human review and approval workflows important?
- Which APIs, webhooks, exports, or integrations would you expect?
- What security or data-retention conditions would need to be met?
- Would SaaS work for you, or would you require an on-premises deployment?
- Would you prefer usage-based, per-page, or fixed pricing?
I’m actively building this and would genuinely appreciate feedback, criticism, and real-world workflow examples.
I’m building a document intelligence system that uses Vision-Language Models to process visually complex documents.
A recurring limitation I’ve noticed with conventional document pipelines is that text extraction alone often loses important context. Tables, charts, diagrams, scanned pages,spatial relationships, and visual hierarchy may carry as much meaning as the text itself.
The approach I’m exploring treats each page as a visual document rather than only a collection of extracted text. The current pipeline focuses on:
- Understanding text, layout, tables, charts, images, and diagrams
- Processing scanned and digitally generated PDFs
- Preserving page-level provenance for citations
- Supporting semantic search across document collections
- Extracting structured information without discarding visual context
- Allowing users to verify results against the original page
Before deciding what to prioritize, I’d like to learn from people who already work with document automation.
What conditions would need to be met before you could use a document intelligence platform with real business documents?
I’m still actively building, so honest technical criticism and real-world workflow examples would be extremely helpful. If anyone is interested in testing an early version,
Feel free to comment or message me.