Edison Scientific’s Kosmos AI Scientist helps researchers navigate complex scientific landscapes to synthesize literature, identify connections and surface evidence.
Edison needed a way to rapidly and accurately extract structured information from large volumes of PDFs, including equations, tables and figures that traditional information parsing methods often mishandle.
By integrating the NVIDIA Nemotron Parse model into its PaperQA pipeline, Edison can decompose research papers, index key concepts and ground responses in specific passages, improving both throughput and answer quality for scientists. This approach turns a sprawling research corpus into an interactive, queryable knowledge engine that accelerates hypothesis generation and literature review.
The high efficiency of Nemotron Parse enables cost-efficient serving at scale, allowing Edison’s team to unlock the whole multimodal pipeline.
A robust, domain-specific document intelligence pipeline requires technologies that can handle data extraction, embedding and reranking, while keeping the data secure and compliant with regulations.
Extraction: Nemotron extraction and OCR models rapidly ingest multimodal PDFs, text, tables, graphs and images to convert them into structured, machine-readable content while preserving layout and semantics.
Embedding: Nemotron embedding models convert passages, entities and visual elements into vector representations tuned for document retrieval, enabling semantically accurate search.
Reranking: Nemotron reranking models evaluate candidate passages to ensure the most relevant content is surfaced as context for large language models (LLMs), improving answer fidelity and reducing hallucinations.
Parsing: Nemotron Parse models decipher document semantics to extract text and tables with precise spatial grounding and correct reading flow. Overcoming layout variability, they turn unstructured documents into actionable data that enhances the accuracy of LLMs and agentic workflows.
These capabilities are packaged as NVIDIA NIM microservices and foundation models that run efficiently on NVIDIA GPUs, allowing teams to scale from proof of concept to production while keeping sensitive data within their chosen cloud or data center environment.
The most effective AI systems use a mix of frontier models and open source models like NVIDIA Nemotron, with an LLM router analyzing each task and automatically selecting the model best suited for it. This approach keeps performance strong while managing computing costs and improving efficiency.
Access a step-by-step tutorial on how to build a document processing pipeline with RAG capabilities. Explore how Nemotron RAG can power specialized agents tailored for different industries.
Plus, experiment with Nemotron RAG models and the NVIDIA NeMo Retriever open library, available on GitHub and Hugging Face , as well as Nemotron Parse on Hugging Face .
Join the community of developers building with the NVIDIA Blueprint for Enterprise RAG — trusted by a dozen industry-leading AI Data Platform providers and available now on build.nvidia.com , GitHub and the NGC catalog .
Stay up to date on agentic AI, NVIDIA Nemotron and more by subscribing to NVIDIA AI news , joining the community and following NVIDIA AI on LinkedIn , Instagram , X and Facebook .
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Key considerations
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Reference reading
- https://blogs.nvidia.com/blog/ai-agents-intelligent-document-processing/#content
- https://www.nvidia.com/en-us/
- https://blogs.nvidia.com/?s=
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