engineering · 1 min read
Multimodal RAG: Indexing Images, PDFs and Tables Side-by-Side
Most enterprise knowledge lives in PDFs with charts and tables. Multimodal RAG actually understands them. Here is how Bhogar AI handles every format.
BABhogar AI TeamProduct & Engineering
Real enterprise documents are messy: charts, tables, scanned pages, footnotes. Text-only RAG silently drops anything that is not body text - and then wonders why it cannot answer questions about a bar chart.
Why it matters
Modern multimodal embeddings and vision-language models can finally do this well. The integration work is the hard part: layout-aware parsing, image+text dual indexing, and citations that point back to the right page region.
How Bhogar AI approaches it
Bhogar AI ships multimodal indexing pipelines for PDFs, slides, scanned documents, and HTML with images. Each chunk stores text, layout context and an image crop; retrieval uses the dual representation; citations highlight the exact region.
- Layout-aware PDF parsing with table and figure detection
- Dual text + image embeddings with hybrid retrieval
- OCR fallback for scanned documents
- Page-region citations for visual evidence
- Pipeline supports PDF, DOCX, PPTX, XLSX, HTML, EPUB
What you get
On visually-rich corpora (financial reports, scientific papers, slide decks) multimodal RAG lifts answerable-question coverage 30-55 points over text-only.