RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/NeMo-Retriever

Default branch main · commit 1cf65a10 · scanned 6/21/2026, 1:01:16 PM

GitHub: 2,941 stars · 327 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface NVIDIA/NeMo-Retriever, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    retrieval-augmented-generation, rag, llm, document-processing, ocr, embedding-generation, nvidia-nim, microservices, knowledge-base, information-extraction
  • highreadme#2
    Reposition the README's opening paragraph to emphasize RAG/LLM

    Why:

    CURRENT
    NeMo Retriever Library is a scalable, performance-oriented framework for document content and metadata extraction. It supports both NVIDIA NIM microservices and a wide range of models to find, contextualize, and extract text, tables, charts, and infographics for use in downstream generative and retrieval-augmented applications.
    COPY-PASTE FIX
    NeMo Retriever Library is a scalable, performance-oriented framework designed to power Retrieval-Augmented Generation (RAG) for Large Language Models (LLMs). It leverages NVIDIA NIM microservices and a wide range of models to find, contextualize, and extract text, tables, charts, and infographics from documents, making them ready for downstream generative applications.
  • mediumabout#3
    Update the repository description to explicitly mention RAG/LLM

    Why:

    CURRENT
    NeMo Retriever Library is a scalable, performance-oriented document content and metadata extraction microservice. NeMo Retriever Library uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images that you can use in downstream generative applications.
    COPY-PASTE FIX
    NeMo Retriever Library is a scalable, performance-oriented framework for document content and metadata extraction, specifically designed to power Retrieval-Augmented Generation (RAG) for LLMs. It uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images for downstream generative applications.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface NVIDIA/NeMo-Retriever
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Document AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Document AI · recommended 2×
  2. Unstructured.io · recommended 1×
  3. Nougat · recommended 1×
  4. LayoutParser · recommended 1×
  5. Tesseract · recommended 1×
  • CATEGORY QUERY
    How to extract text, tables, and images from documents for retrieval-augmented generation?
    you: not recommended
    AI recommended (in order):
    1. Unstructured.io
    2. Nougat
    3. LayoutParser
    4. Tesseract
    5. PaddleOCR
    6. Camelot
    7. Tabula-py
    8. PyMuPDF
    9. Apache Tika
    10. Microsoft Azure AI Document Intelligence
    11. Google Cloud Document AI

    AI recommended 11 alternatives but never named NVIDIA/NeMo-Retriever. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are scalable frameworks for document content extraction, OCR, and embedding generation?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Document AI
    2. Vertex AI
    3. Amazon Textract
    4. Amazon Comprehend
    5. Amazon SageMaker
    6. Microsoft Azure Form Recognizer
    7. Azure Cognitive Services for Language
    8. Azure Machine Learning
    9. Tesseract OCR (tesseract-ocr/tesseract)
    10. pytesseract (madmaze/pytesseract)
    11. spaCy (explosion/spaCy)
    12. Hugging Face Transformers (huggingface/transformers)
    13. PaddleOCR (PaddlePaddle/PaddleOCR)
    14. Faiss (facebookresearch/faiss)
    15. Weaviate (weaviate/weaviate)

    AI recommended 15 alternatives but never named NVIDIA/NeMo-Retriever. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of NVIDIA/NeMo-Retriever?
    pass
    AI named NVIDIA/NeMo-Retriever explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts NVIDIA/NeMo-Retriever in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA/NeMo-Retriever explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo NVIDIA/NeMo-Retriever solve, and who is the primary audience?
    pass
    AI named NVIDIA/NeMo-Retriever explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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NVIDIA/NeMo-Retriever — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite