Upload a document. Ask it anything, with proof.
Standard RAG systems only understand text — they fail on scanned PDFs, invoices, and reports where the visual layout carries meaning, forcing manual review of every document.
A multimodal document intelligence system that reads the visual layout of each page with GPT-4o Vision before answering — every answer traces back to the exact page it came from.
View the source — VisionRAG on GitHub
View Source ↗Document Upload -> Page-Level Vision Embedding (GPT-4o Vision) -> pgvector Similarity Search -> Cited Answer
For simple business AI implementations — chatbot, FAQ assistant, basic knowledge base.
Workflow automation, AI agent, API/Telegram/email integrations.
RAG, NL2SQL, business analytics, document intelligence, advanced agents.
For larger or more complex requirements — scoped on a call.
Directional, not a fixed quote — real scope depends on your data and integrations.
Scanned PDFs, invoices, contracts, and reports — anything where the page layout carries meaning, not just plain text extraction.
Every answer cites the specific document and page it came from — a core design requirement, not an afterthought, per the VisionRAG case study.
No — it's built specifically to handle scanned and image-heavy documents using vision-based page understanding, not just text extraction that fails on scans.
Tell me what you're trying to solve — I'll tell you honestly whether this is the right fit.