RRepoGEO

REPOGEO REPORT · LITE

NanoNets/docstrange

Default branch main · commit d72ce262 · scanned 5/18/2026, 5:48:01 AM

GitHub: 1,476 stars · 132 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
27 /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
1 / 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 NanoNets/docstrange, 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 README's opening statement to emphasize AI/LLM and local processing

    Why:

    CURRENT
    DocStrange converts documents to Markdown, JSON, CSV, and HTML quickly and accurately.
    COPY-PASTE FIX
    DocStrange is an AI-powered document processing engine that converts complex documents (PDFs, images, office files, URLs) into structured data (Markdown, JSON, CSV, HTML) using advanced OCR and an upgraded 7B LLM, offering both a free cloud API and 100% private local processing.
  • mediumreadme#2
    Add a clear differentiator statement in the README

    Why:

    COPY-PASTE FIX
    Unlike many cloud-only solutions, DocStrange offers a 100% private local processing mode, ensuring data privacy, while its 7B LLM is specifically optimized for generating clean, structured output for downstream AI applications.
  • mediumtopics#3
    Refine topics to include more specific AI/LLM and privacy-focused terms

    Why:

    CURRENT
    ai, document-parser, document-parsing, image-to-markdown, llm, markdown, ocr, pdf-parser, pdf-to-json, pdf-to-markdown, structured-data, structured-data-capture, tables
    COPY-PASTE FIX
    ai, document-parser, document-parsing, image-to-markdown, llm, markdown, ocr, pdf-parser, pdf-to-json, pdf-to-markdown, structured-data, structured-data-capture, tables, document-ai, llm-applications, private-ai, local-llm, data-extraction-ai

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 NanoNets/docstrange
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. Microsoft Azure Form Recognizer · recommended 1×
  4. Rossum · recommended 1×
  5. Tesseract OCR · recommended 1×
  • CATEGORY QUERY
    How can I extract structured data from PDFs, images, and office documents into JSON?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Document AI
    2. Amazon Textract
    3. Microsoft Azure Form Recognizer
    4. Rossum
    5. Tesseract OCR
    6. PyTesseract
    7. PyPDF2
    8. pdfminer.six
    9. spaCy
    10. NLTK
    11. Apache POI
    12. OpenPyXL
    13. NPOI
    14. PDF.co

    AI recommended 14 alternatives but never named NanoNets/docstrange. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools convert various document formats to clean, LLM-optimized markdown with OCR?
    you: not recommended
    AI recommended (in order):
    1. Nougat (facebookresearch/nougat)
    2. Azure AI Document Intelligence
    3. Google Cloud Document AI
    4. Amazon Textract
    5. Tesseract OCR (tesseract-ocr/tesseract)
    6. python-docx (python-openxml/python-docx)
    7. PyPDF2 (py-pdf/PyPDF2)
    8. pdfminer.six (pdfminer/pdfminer.six)
    9. Pandoc (jgm/pandoc)

    AI recommended 9 alternatives but never named NanoNets/docstrange. 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 NanoNets/docstrange?
    pass
    AI did not name NanoNets/docstrange — likely talking about a different project

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

  • If a team adopts NanoNets/docstrange in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NanoNets/docstrange 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 NanoNets/docstrange solve, and who is the primary audience?
    pass
    AI did not name NanoNets/docstrange — likely talking about a different project

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

Embed your GEO score

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