RRepoGEO

REPOGEO REPORT · LITE

katanaml/sparrow

Default branch main · commit 19c67f6f · scanned 5/17/2026, 2:03:16 PM

GitHub: 5,159 stars · 516 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
    Refine the 'about' description for clarity on domain

    Why:

    CURRENT
    Structured data extraction and instruction calling with ML, LLM and Vision LLM
    COPY-PASTE FIX
    Enterprise-grade structured data extraction from documents and images using ML, LLM, and Vision LLM APIs.
  • hightopics#2
    Add domain-specific topics

    Why:

    CURRENT
    computer-vision, gpt, huggingface-transformers, llm, machinelearning, nlp-machine-learning, rag, vllm
    COPY-PASTE FIX
    Add document-intelligence, data-extraction, invoice-processing, receipt-scanning, form-processing, on-premise-ai, enterprise-ai to the existing topics.
  • mediumreadme#3
    Add a 'Why Sparrow?' or 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section (e.g., 'Why Sparrow?' or 'Key Differentiators') to the README with text like: 'Unlike cloud-based solutions such as Google Document AI or AWS Textract, Sparrow is an API-first platform designed for enterprise document intelligence, running entirely on your own infrastructure with no external API calls or cloud dependencies. This ensures maximum privacy, control, and compliance for your structured data extraction workflows.'

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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Document AI · recommended 1×
  2. Azure AI Document Intelligence · recommended 1×
  3. AWS Textract · recommended 1×
  4. OpenAI GPT-4o / GPT-4 Turbo with Vision · recommended 1×
  5. Anthropic Claude 3 Opus / Sonnet · recommended 1×
  • CATEGORY QUERY
    How to extract structured data from invoices and images using LLMs?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Document AI
    2. Azure AI Document Intelligence
    3. AWS Textract
    4. OpenAI GPT-4o / GPT-4 Turbo with Vision
    5. Anthropic Claude 3 Opus / Sonnet
    6. Llama 3
    7. Mixtral
    8. Nougat

    AI recommended 8 alternatives but never named katanaml/sparrow. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide on-premise structured document processing and data extraction APIs?
    you: not recommended
    AI recommended (in order):
    1. ABBYY FineReader Engine SDK
    2. Kofax Transformation Modules (KTM)
    3. OpenText Intelligent Capture
    4. IBM Datacap
    5. Ephesoft Transact
    6. Tesseract OCR

    AI recommended 6 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.

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