What is Multimodal RAG?
Multimodal RAG is an advanced form of Retrieval-Augmented Generation that retrieves and reasons over multiple data types including images, PDFs, tables, charts, and diagrams alongside text. This enables AI systems to answer questions using visual and structured information from business documents, not just plain text, delivering more complete and accurate insights.
What Is Multimodal RAG?
Multimodal RAG (Retrieval-Augmented Generation) is an extension of standard RAG that enables AI systems to retrieve and process multiple types of information -- images, charts, tables, PDFs, diagrams, and text -- when answering questions. Standard RAG systems search through text documents to find relevant information and feed it to an AI model for generating answers. Multimodal RAG does the same thing but includes visual and structured content as well, enabling the AI to reason over charts in a financial report, diagrams in a technical manual, or photos in an inspection record.
For business leaders, the distinction is critical. Most real business documents are not just text. Annual reports contain charts and graphs. Technical manuals include diagrams and schematics. Marketing materials mix text with images. Product catalogs combine descriptions with photographs. A text-only RAG system ignores all this visual information. Multimodal RAG includes it, delivering more complete and accurate answers.
How Multimodal RAG Works
Multimodal RAG extends the standard RAG architecture to handle multiple data types:
- Multimodal ingestion: Documents are processed to extract and index not just text but also images, tables, charts, and other visual elements. This might involve converting PDFs to images, extracting tables into structured formats, and generating descriptions of visual content
- Cross-modal embedding: Both text and visual content are converted into numerical representations (embeddings) in a shared space, enabling the system to find relevant information regardless of whether it exists as text, an image, or a table
- Multimodal retrieval: When a user asks a question, the system searches across all content types to find the most relevant information, whether that is a paragraph of text, a chart, a table, or a photograph
- Multimodal reasoning: A vision-language model processes both the retrieved text and visual content together, enabling it to reference a chart, read a table, or interpret a diagram when formulating its answer
Why Multimodal RAG Matters for Business
Complete document understanding Business documents are inherently multimodal. A quarterly financial report might have the key insight in a chart, not in the text. A compliance document might reference a decision tree diagram. A product specification sheet includes both descriptions and technical drawings. Multimodal RAG ensures the AI considers all available information, not just the text portions.
Accurate financial analysis Financial documents are heavily reliant on tables, charts, and structured data. A CFO asking "What was our revenue growth trend in Q3?" needs the AI to interpret a revenue chart or read a financial table, not just search for the word "revenue" in surrounding text. Multimodal RAG makes this possible.
Visual inspection and quality records Manufacturing companies across Southeast Asia maintain quality inspection records that include photographs, diagrams, and technical measurements alongside written notes. Multimodal RAG enables AI systems to search and reason over these complete records rather than only the text portions.
Knowledge management at scale Large organizations accumulate knowledge in diverse formats: slide decks, whitepapers, engineering drawings, photographed whiteboards, and scanned documents. Multimodal RAG makes this entire knowledge base searchable and queryable through natural language, regardless of the original format.
Key Examples and Use Cases
Professional services: Consulting firms in Singapore can build Multimodal RAG systems that search through client presentation decks, analyzing both the text and visual charts to answer questions about project histories, benchmarking data, and strategic recommendations.
Healthcare: Medical facilities across ASEAN can create AI systems that retrieve and reason over patient records including medical images, lab result tables, and clinical notes together, providing a more complete picture for clinical decision support.
Real estate and construction: Property companies like CapitaLand or Sinar Mas can deploy Multimodal RAG to search through property documents that include floor plans, site photographs, zoning maps, and text descriptions, enabling natural language queries like "Show me all properties with more than 500 square meters of retail space on the ground floor."
Manufacturing and engineering: Companies operating factories across Indonesia, Vietnam, and Thailand can build systems that search through technical manuals including engineering diagrams, maintenance photographs, and parts catalogs to help technicians troubleshoot equipment issues.
Insurance claims: Insurance companies across the region can process claims that include damage photographs, medical documents, and policy terms together, enabling AI to assess claims with access to both visual evidence and policy language.
Getting Started
- Audit your document landscape: Identify which business-critical documents contain important visual information that text-only RAG would miss -- financial reports, technical manuals, product catalogs, and presentation decks are common candidates
- Choose the right foundation: Select AI models with strong vision capabilities, such as GPT-4o, Claude, or Gemini, which can reason over both text and images effectively
- Start with a focused pilot: Begin with a single document type, such as financial reports or technical manuals, and build a Multimodal RAG system for that specific use case before expanding
- Invest in document processing: The quality of Multimodal RAG depends heavily on how well documents are parsed into their component parts -- text, images, tables, and charts all need clean extraction
- Measure the improvement: Compare answer quality between text-only RAG and Multimodal RAG on a representative set of questions to quantify the business value of including visual content
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- Most business documents contain critical information in charts, tables, images, and diagrams that text-only RAG systems completely miss, making Multimodal RAG essential for comprehensive document intelligence
- Building a Multimodal RAG system requires stronger document processing pipelines than text-only RAG, so budget additional time and resources for the ingestion and indexing components
- Start with document types where visual information is most business-critical, such as financial reports and technical manuals, to demonstrate clear ROI before expanding to other content types
Frequently Asked Questions
How is Multimodal RAG different from regular RAG?
Regular RAG retrieves and reasons over text only. If your financial report has a chart showing declining revenue, regular RAG cannot see or interpret that chart -- it can only find text that mentions revenue. Multimodal RAG processes and indexes images, charts, tables, and diagrams alongside text, so it can retrieve and reason over the chart directly. This means the AI can answer questions that require understanding visual information, not just reading words.
What types of documents benefit most from Multimodal RAG?
Documents where key information exists in visual form benefit most: financial reports with charts and tables, technical manuals with diagrams and schematics, product catalogs with images and specifications, medical records with imaging and lab results, and presentation decks where insights are conveyed through graphics. If stripping all images and formatting from a document would lose important information, that document is a strong candidate for Multimodal RAG.
More Questions
Yes, with appropriate expectations. The underlying vision-language models from leading providers are mature enough for production use, and the document processing tools are improving rapidly. The main challenges are in document ingestion quality -- accurately extracting charts, tables, and images from complex PDF layouts -- and in managing the additional computational cost of processing visual content. Starting with a focused pilot on well-structured documents is the recommended approach for most businesses.
Need help implementing Multimodal RAG?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how multimodal rag fits into your AI roadmap.