Multimodal RAG Development Services
RAG that reads the charts, tables and scans too.
AI solutions that understand not just text, but images, scanned PDFs, tables, charts and complex visual documents - and reason across all of them.
Half of enterprise knowledge lives in things text search can't read: scanned forms, diagrams, financial tables, product photos. Multimodal RAG unlocks them.
We build retrieval systems that understand documents the way a person does - parsing layout, reading tables and charts, interpreting images and scanned pages, and preserving the relationships between them.
That means an assistant that can answer 'what does this diagram show', 'summarise this scanned contract', or 'compare the figures in these two financial tables' - grounded in the visual source, with citations back to the exact page and region.
What we build
Understanding beyond plain text
Layout, tables, charts and images treated as first-class content - not thrown away at ingestion.
Visual document parsing
Extract structure, tables and layout from complex PDFs and scans without flattening them into unusable text.
Image & diagram understanding
Retrieve and reason over photos, screenshots, schematics and diagrams alongside text.
Table & chart extraction
Pull figures out of tables and charts so they can be queried, compared and summarised.
OCR for scans
Turn scanned and handwritten documents into searchable, analysable content.
Cross-modal retrieval
Answer questions that span text and visuals, citing the exact page and region used.
Grounded generation
Responses tied to visual sources, reducing hallucination on documents text-only systems misread.
How we deliver
A path from idea to production
A pragmatic engagement model that de-risks adoption and gets a working system in front of your users fast.
- 01
Content survey
We assess your document types - scans, tables, diagrams - and where text-only RAG falls short today.
- 02
Multimodal pipeline
We build parsing, OCR and multimodal embedding tuned to your document layouts.
- 03
Reasoning & eval
We add cross-modal retrieval and evaluate faithfulness on your hardest documents.
- 04
Deploy
We ship the assistant with page- and region-level citations, and keep it accurate as documents evolve.
Tools & stack
Technologies we work with
We stay model- and vendor-flexible, choosing the stack that fits your data, budget, and compliance needs.
- Vision-language models
- GPT-4o / Claude vision
- Gemini multimodal
- ColPali / ColQwen
- LlamaIndex
- docTR / Tesseract OCR
- Unstructured
- AWS Textract
- pgvector
- Qdrant
Where it fits
Use cases & industries
For document-heavy industries where the important data is trapped in visuals.
Financial documents
Query figures inside statements, filings and complex tables.
Insurance & claims
Read scanned forms, photos and handwritten notes into structured answers.
Engineering & manuals
Search diagrams, schematics and illustrated technical documentation.
Healthcare records
Interpret scanned charts and mixed text-image records within compliance rules.
Real estate & legal
Extract data from scanned contracts, floor plans and deeds.
Research
Reason over papers with figures, tables and charts, not just abstracts.
Common questions
Standard RAG only reads extractable text and discards layout, tables and images. Multimodal RAG understands those visual elements and reasons across them.
Yes. We combine OCR with vision-language models and tune the pipeline on your document quality to maximise accuracy.
Yes — down to the specific page and region of the source document, so answers stay verifiable.
Get a free personal AI consultation?
Free ConsultationExplore more services
- Hire Dedicated Developers
We help startups, SMEs, and enterprises hire skilled, pre-vetted dedicated developers fully integrated with your team, working on your timezone, delivering results from day one.
- MCP Integration Services
Seamless integration between AI applications and your enterprise tools, APIs, databases and external services - via the open Model Context Protocol.
- LLM Fine-Tuning Services
Fine-tuning language models on your domain-specific data for better accuracy, on-brand behaviour and lower inference cost on the tasks you run most.