RRepoGEO

REPOGEO REPORT · LITE

whyhow-ai/knowledge-table

Default branch main · commit cc347497 · scanned 6/2/2026, 10:37:14 AM

GitHub: 670 stars · 97 forks

AI VISIBILITY SCORE
28 /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
2 / 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 whyhow-ai/knowledge-table, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify the core differentiator and target use case in the README's opening

    Why:

    CURRENT
    Knowledge Table is an open-source package designed to simplify extracting and exploring structured data from unstructured documents. It enables the creation of structured knowledge representations, such as tables and graphs, using a natural language query interface. With customizable extraction rules, fine-tuned formatting options, and data traceability through provenance displayed in the UI, Knowledge Table is adaptable to various use cases.
    COPY-PASTE FIX
    **Knowledge Table** is an open-source, Python-native package designed to simplify extracting and exploring structured data from unstructured documents, specifically optimized for **RAG workflows and LLM applications**. It enables the creation of structured knowledge representations, such as tables and graphs, using a natural language query interface. Unlike heavy document processing services, Knowledge Table provides a lightweight, spreadsheet-like interface for business users and a flexible, configurable backend for developers to easily integrate precise factual data into their AI systems.
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://whyhow.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 whyhow-ai/knowledge-table
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Unstructured-IO/unstructured
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Unstructured-IO/unstructured · recommended 1×
  2. LlamaParse · recommended 1×
  3. Microsoft Azure Form Recognizer / Azure AI Document Intelligence · recommended 1×
  4. Google Cloud Document AI · recommended 1×
  5. Amazon Textract · recommended 1×
  • CATEGORY QUERY
    How to extract structured data from unstructured documents for RAG workflows?
    you: not recommended
    AI recommended (in order):
    1. Unstructured.io (Unstructured-IO/unstructured)
    2. LlamaParse
    3. Microsoft Azure Form Recognizer / Azure AI Document Intelligence
    4. Google Cloud Document AI
    5. Amazon Textract
    6. Nougat (facebookresearch/nougat)
    7. PyMuPDF (pymupdf/PyMuPDF)

    AI recommended 7 alternatives but never named whyhow-ai/knowledge-table. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source tool simplifies creating knowledge tables from text with a user-friendly interface?
    you: not recommended
    AI recommended (in order):
    1. OpenRefine
    2. Doccano
    3. Prodigy
    4. TagUI
    5. Haystack
    6. KNIME Analytics Platform

    AI recommended 6 alternatives but never named whyhow-ai/knowledge-table. 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 whyhow-ai/knowledge-table?
    pass
    AI did not name whyhow-ai/knowledge-table — 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 whyhow-ai/knowledge-table in production, what risks or prerequisites should they evaluate first?
    pass
    AI named whyhow-ai/knowledge-table 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 whyhow-ai/knowledge-table solve, and who is the primary audience?
    pass
    AI named whyhow-ai/knowledge-table explicitly

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

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