RRepoGEO

REPOGEO REPORT · LITE

icereed/paperless-gpt

Default branch main · commit 94c84287 · scanned 5/25/2026, 8:47:11 PM

GitHub: 2,369 stars · 166 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 icereed/paperless-gpt, 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
  • highabout#1
    Reposition the 'About' description to clarify its role as a Paperless-ngx companion

    Why:

    CURRENT
    Use LLMs and LLM Vision (OCR) to handle paperless-ngx - Document Digitalization powered by AI
    COPY-PASTE FIX
    An AI companion for Paperless-ngx: uses LLMs and LLM Vision (OCR) to automate document digitalization, titles, and tags.
  • highhomepage#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://your-project-website.com
  • mediumreadme#3
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    ## How is paperless-gpt different from Cloud OCR services (e.g., Google Document AI, Amazon Textract)?
paperless-gpt is not a standalone cloud platform for general document processing. Instead, it's an intelligent companion designed specifically to enhance your existing Paperless-ngx setup. While cloud services offer broad OCR and document intelligence, paperless-gpt focuses on deeply integrating LLM-powered OCR and metadata generation directly into your Paperless-ngx workflow, providing specialized automation and accuracy for your scanned documents within that ecosystem.

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 icereed/paperless-gpt
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. Amazon Textract · recommended 2×
  3. Azure AI Document Intelligence · recommended 2×
  4. Microsoft Azure Form Recognizer · recommended 1×
  5. OpenCV · recommended 1×
  • CATEGORY QUERY
    How to automate document organization and tagging using AI for scanned papers?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Document AI
    2. Amazon Textract
    3. Microsoft Azure Form Recognizer
    4. Azure AI Document Intelligence
    5. OpenCV
    6. Tesseract OCR
    7. Python
    8. scikit-learn
    9. spaCy
    10. NLTK
    11. UiPath Document Understanding
    12. ABBYY FineReader Engine
    13. ABBYY
    14. Google
    15. Microsoft
    16. Kofax Capture
    17. Kofax TotalAgility

    AI recommended 17 alternatives but never named icereed/paperless-gpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enhance OCR accuracy and automate document metadata generation with LLMs?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Document AI
    2. Azure AI Document Intelligence
    3. Amazon Textract
    4. Amazon Comprehend
    5. Amazon SageMaker
    6. Llama 2
    7. Falcon
    8. OpenAI GPT-4
    9. OpenAI GPT-3.5
    10. Tesseract
    11. Abbyy FineReader Engine
    12. Kofax OmniPage Capture SDK
    13. Hugging Face Transformers
    14. Donut
    15. LayoutLMv3
    16. Mistral
    17. Flan-T5
    18. Nanonets
    19. Rossum

    AI recommended 19 alternatives but never named icereed/paperless-gpt. 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 icereed/paperless-gpt?
    pass
    AI named icereed/paperless-gpt explicitly

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

  • If a team adopts icereed/paperless-gpt in production, what risks or prerequisites should they evaluate first?
    pass
    AI named icereed/paperless-gpt 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 icereed/paperless-gpt solve, and who is the primary audience?
    pass
    AI named icereed/paperless-gpt explicitly

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

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icereed/paperless-gpt — RepoGEO report