Idea
Open-source multimodal retrieval and extraction platform enabling scalable, accurate data processing for enterprises and biomedical researchers
Research Paper
Core Innovation
This paper presents MMORE, a pipeline that unifies processing of over fifteen multimodal file types into a single format for large language model applications. It introduces modular, distributed processing for scalable parallelization and hybrid dense-sparse retrieval, achieving significant speed and accuracy improvements over prior methods. MMORE also supports interactive APIs and batch endpoints, enhancing usability and biomedical QA performance.
Market Size (TAM)
$10–20B TAM, $2–5B SAM; assumption: growing demand for multimodal AI data processing and biomedical QA solutions in enterprises and research institutions.
Potential Customers & Pain Points
- Enterprises handling diverse document types needing unified data extraction
- Biomedical researchers requiring improved QA accuracy
- AI developers seeking scalable multimodal retrieval solutions
Business Model
Open-source core with enterprise-grade support, custom integration services, and hosted API subscriptions for scalable multimodal data processing.
Competitive Landscape
- Pinecone
- Weaviate
- Haystack
Implementation Challenges
- Integration complexity with existing enterprise systems
- Competition from established multimodal retrieval platforms
- Need for continuous updates to support new file types and modalities
Validation Strategy
- Deploy pilot with biomedical research labs to benchmark QA improvements
- Partner with enterprises to test scalability and integration in real-world workflows
- Collect user feedback to refine APIs and expand modality support
Research Paper Overview
MMORE: Massive Multimodal Open RAG & Extraction
Summary
MMORE is an open-source pipeline for large-scale multimodal retrieval-augmented generation and extraction that processes over fifteen file types including text, tables, images, emails, audio, and video into a unified format for LLM applications. It features modular, distributed processing for scalable parallelization on CPUs and GPUs, achieving 3.8x speedup over single-node baselines and 40% higher accuracy than Docling on scanned PDFs. MMORE integrates hybrid dense-sparse retrieval and supports interactive APIs and batch endpoints, improving biomedical QA accuracy with deeper retrieval on PubMedQA. The codebase is available at https://github.com/swiss-ai/mmore.