RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/raft

Default branch main · commit 1240331e · scanned 6/28/2026, 1:36:26 AM

GitHub: 1,018 stars · 236 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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/raft, 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
  • highreadme#1
    Reposition the core 'What is RAFT?' definition to the top of the README

    Why:

    CURRENT
    # <div align="left">&nbsp;RAFT: Reusable Accelerated Functions and Tools</div>

<p align="center">
  
</p>

## Contents
<hr>

1. [Useful Resources](#useful-resources)
2. [What is RAFT?](#what-is-raft)
...
    COPY-PASTE FIX
    # <div align="left">&nbsp;RAFT: Reusable Accelerated Functions and Tools</div>

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

<p align="center">
  
</p>

## Contents
<hr>

1. [Useful Resources](#useful-resources)
2. [Use cases](#use-cases)
...
  • mediumreadme#2
    Add a 'Why RAFT?' or 'RAFT's Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., 'Why RAFT?' or 'RAFT's Differentiators', that clearly articulates its role as a collection of highly optimized, GPU-accelerated C++/CUDA primitives and algorithms, explaining how it complements or differs from higher-level frameworks and other specialized libraries.
  • lowreadme#3
    Refine the 'Use cases' section to highlight RAFT's unique value

    Why:

    COPY-PASTE FIX
    Review and expand the 'Use cases' section to explicitly detail scenarios where RAFT's GPU-accelerated primitives are the ideal choice, emphasizing its role in building high-performance ML/IR/Vector Search applications, potentially with concrete examples that highlight its unique value proposition as a building block library.

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/raft
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. JAX · recommended 2×
  4. RAPIDS · recommended 2×
  5. cuML · recommended 2×
  • CATEGORY QUERY
    How can I accelerate fundamental machine learning and information retrieval algorithms using GPUs?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. JAX
    4. RAPIDS
    5. cuML
    6. cuDF
    7. cuGraph
    8. Numba
    9. OpenCV
    10. CUDA C/C++

    AI recommended 10 alternatives but never named NVIDIA/raft. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What libraries provide CUDA-enabled building blocks for high-performance machine learning applications and vector search?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. FAISS
    4. RAPIDS
    5. cuML
    6. cuDF
    7. cuGraph
    8. JAX
    9. NVIDIA DALI

    AI recommended 9 alternatives but never named NVIDIA/raft. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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/raft?
    pass
    AI named NVIDIA/raft 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/raft in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA/raft 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/raft solve, and who is the primary audience?
    pass
    AI named NVIDIA/raft 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/raft — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite