RRepoGEO

REPOGEO REPORT · LITE

katanaml/sparrow

Default branch main · commit ba52670c · scanned 6/28/2026, 6:31:48 PM

GitHub: 5,170 stars · 517 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
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 katanaml/sparrow, 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
    Clarify the 'About' description to emphasize core purpose

    Why:

    CURRENT
    Structured data extraction, instruction calling and agentic workflows with ML, LLM and Vision LLM
    COPY-PASTE FIX
    API-first platform for enterprise document intelligence: structured data extraction, instruction calling, and agentic workflows with ML, LLM, and Vision LLM.
  • mediumreadme#2
    Add a descriptive tagline to the README H1

    Why:

    CURRENT
    # Sparrow
    COPY-PASTE FIX
    # Sparrow: API-first Platform for Enterprise Document Intelligence
  • mediumtopics#3
    Expand topics with specific document processing keywords

    Why:

    CURRENT
    agentic-ai, computer-vision, documentai, huggingface-transformers, llm, machinelearning, vllm
    COPY-PASTE FIX
    agentic-ai, computer-vision, documentai, huggingface-transformers, llm, machinelearning, vllm, structured-data-extraction, document-processing, invoice-processing, form-processing

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 katanaml/sparrow
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 1×
  4. OpenAI GPT-4 · recommended 1×
  5. Claude 3 · recommended 1×
  • CATEGORY QUERY
    How to extract structured data from various document types using AI models?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Document AI
    2. Amazon Textract
    3. Azure AI Document Intelligence
    4. OpenAI GPT-4
    5. Claude 3
    6. Llama 3
    7. SpaCy
    8. Tesseract OCR
    9. pytesseract
    10. LayoutLMv3
    11. Hugging Face Transformers

    AI recommended 11 alternatives but never named katanaml/sparrow. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best API-first platforms for automating document processing and intelligent data extraction?
    you: not recommended
    AI recommended (in order):
    1. Rossum
    2. Hyperscience
    3. Amazon Textract
    4. Google Cloud Document AI
    5. Microsoft Azure Form Recognizer
    6. Nanonets
    7. Abbyy Vantage

    AI recommended 7 alternatives but never named katanaml/sparrow. 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 katanaml/sparrow?
    pass
    AI named katanaml/sparrow explicitly

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

  • If a team adopts katanaml/sparrow in production, what risks or prerequisites should they evaluate first?
    pass
    AI named katanaml/sparrow 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 katanaml/sparrow solve, and who is the primary audience?
    pass
    AI named katanaml/sparrow 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 katanaml/sparrow. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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katanaml/sparrow — 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