RRepoGEO

REPOGEO REPORT · LITE

run-llama/ParseBench

Default branch main · commit b4750d44 · scanned 6/21/2026, 2:23:36 PM

GitHub: 501 stars · 63 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 run-llama/ParseBench, 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 README's opening to emphasize public leaderboard and AI agent focus

    Why:

    CURRENT
    **ParseBench** is a benchmark for evaluating how well document parsing tools convert PDFs into structured output that AI agents can reliably act on.
    COPY-PASTE FIX
    **ParseBench** is the leading public benchmark and live leaderboard for evaluating how well document parsing tools convert PDFs into structured output that AI agents can reliably act on.
  • mediumreadme#2
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

Unlike general document parsing services (e.g., Google Cloud Document AI, Amazon Textract) which provide a parsing solution, ParseBench is a dedicated benchmark for *evaluating* these tools. Similarly, while it utilizes datasets, ParseBench is distinct from raw datasets (e.g., DocILE, FUNSD) by offering a comprehensive evaluation framework with a live leaderboard and specific metrics tailored for AI agent workflows.
  • lowtopics#3
    Expand GitHub topics with more specific evaluation and AI agent terms

    Why:

    CURRENT
    benchmark, document-ai, document-parsing, evaluation, llamaindex, llm, machine-learning, ocr, pdf-parsing, table-extraction, vision-language-models
    COPY-PASTE FIX
    benchmark, document-ai, document-parsing, evaluation, llamaindex, llm, machine-learning, ocr, pdf-parsing, table-extraction, vision-language-models, ai-agent-benchmarking, document-parsing-evaluation, llm-parsing-benchmarks

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 run-llama/ParseBench
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. Amazon Textract · recommended 1×
  3. Azure AI Document Intelligence · recommended 1×
  4. Rossum · recommended 1×
  5. Hyperscience · recommended 1×
  • CATEGORY QUERY
    How to evaluate document parsing tools for AI agent workflows and structured output?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Document AI
    2. Amazon Textract
    3. Azure AI Document Intelligence
    4. Rossum
    5. Hyperscience
    6. Nanonets
    7. Tesseract OCR

    AI recommended 7 alternatives but never named run-llama/ParseBench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best benchmarks for assessing PDF to structured data extraction quality?
    you: not recommended
    AI recommended (in order):
    1. DocILE Benchmark
    2. FUNSD Dataset
    3. SROIE Dataset
    4. PubTables-1M Dataset
    5. TabFact Dataset
    6. XFUND Dataset

    AI recommended 6 alternatives but never named run-llama/ParseBench. 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 run-llama/ParseBench?
    pass
    AI named run-llama/ParseBench explicitly

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

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

    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