REPOGEO REPORT · LITE

NVIDIA/NeMo-Retriever

Default branch main · commit d85ca1c9 · scanned 5/11/2026, 9:06:24 AM

GitHub: 2,923 stars · 320 forks

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 relevant topics to the repository

Why:

COPY-PASTE FIX

rag, document-extraction, ocr, nlp, nvidia, microservices, enterprise-ai, scalable, data-processing

mediumreadme#2

Emphasize NVIDIA's unique value proposition in the README's opening

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

NVIDIA NeMo Retriever Library is an enterprise-grade, scalable framework for high-performance document content and metadata extraction, specifically optimized for NVIDIA GPUs and NIM microservices. It enables robust extraction of text, tables, charts, and infographics, contextualized via OCR, for production-level generative AI and retrieval-augmented generation (RAG) applications.

lowcomparison#3

Add a section comparing NeMo Retriever to common alternatives

Why:

COPY-PASTE FIX

Add a new section titled 'Why NeMo Retriever?' or 'Comparison to Alternatives' that highlights its unique advantages, such as:
- **NVIDIA Optimization:** Built for NVIDIA GPUs and NIM microservices for unparalleled performance and scalability.
- **Enterprise-Grade:** Designed for production environments with Kubernetes deployment support.
- **Comprehensive Extraction:** Beyond text, extracts tables, charts, and infographics with OCR contextualization.
- **Integrated Ecosystem:** Part of the broader NVIDIA NeMo framework for seamless RAG pipeline development.

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

Of all named tools, what % are you?

Top rival

Google Cloud Document AI

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Google Cloud Document AI · recommended 2×
Amazon Textract · recommended 2×
LlamaParse · recommended 1×
Unstructured.io · recommended 1×
Azure AI Document Intelligence · recommended 1×

CATEGORY QUERY
What tools can extract structured data from diverse documents for RAG applications?
you: not recommended
AI recommended (in order):
1. LlamaParse
2. Unstructured.io
3. Azure AI Document Intelligence
4. Google Cloud Document AI
5. Amazon Textract
6. Nougat
7. PyMuPDF
AI recommended 7 alternatives but never named NVIDIA/NeMo-Retriever. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a high-performance library to extract text, tables, and images from large document sets.
you: not recommended
AI recommended (in order):
1. Apache Tika
2. PDFMiner.six
3. Tabula-py
4. Google Cloud Document AI
5. Amazon Textract
6. Microsoft Azure Form Recognizer
7. OpenCV
AI recommended 7 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?

Embed your GEO score

Drop this badge into the README of NVIDIA/NeMo-Retriever. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/NVIDIA/NeMo-Retriever.svg)](https://repogeo.com/en/r/NVIDIA/NeMo-Retriever)

HTML

<a href="https://repogeo.com/en/r/NVIDIA/NeMo-Retriever"><img src="https://repogeo.com/badge/NVIDIA/NeMo-Retriever.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

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