RRepoGEO

REPOGEO REPORT · LITE

nduckmink/arkon

Default branch main · commit 5e4069df · scanned 6/4/2026, 5:12:44 PM

GitHub: 940 stars · 221 forks

AI VISIBILITY SCORE
35 /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
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 nduckmink/arkon, 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
    Add a concise, differentiating tagline to the README's introduction

    Why:

    COPY-PASTE FIX
    Add this line immediately after the H1: "This is *not* a web framework. Arkon is an actively developed, self-hosted enterprise AI knowledge hub."
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    [Your project's official website URL, e.g., https://arkon.ai]
  • lowreadme#3
    Clarify the project's license(s) directly in the README

    Why:

    COPY-PASTE FIX
    Add a section like: "## License 
 Arkon is licensed under [Specify License Name(s) here, e.g., 'a custom license combining Apache 2.0 and MIT terms']. Please see the [LICENSE](LICENSE) file for full details."

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 nduckmink/arkon
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PostgreSQL
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PostgreSQL · recommended 2×
  2. Confluence Data Center/Server · recommended 1×
  3. K15t Scroll Documents · recommended 1×
  4. Comalatech Comala Document Management · recommended 1×
  5. OpenSearch · recommended 1×
  • CATEGORY QUERY
    How to build a self-hosted knowledge base for enterprise RAG with LLM access control?
    you: not recommended
    AI recommended (in order):
    1. Confluence Data Center/Server
    2. K15t Scroll Documents
    3. Comalatech Comala Document Management
    4. OpenSearch
    5. Elasticsearch
    6. LangChain
    7. LlamaIndex
    8. Sentence-BERT
    9. OpenAI's `text-embedding-ada-002`
    10. PostgreSQL
    11. pgvector
    12. Milvus
    13. Zilliz Cloud
    14. Weaviate
    15. SharePoint Server/OneDrive for Business
    16. Microsoft Graph API
    17. `sentence-transformers/all-MiniLM-L6-v2`
    18. `BAAI/bge-large-en-v1.5`
    19. Llama 2
    20. Mistral
    21. OpenAI's GPT-4
    22. Anthropic's Claude
    23. Active Directory
    24. Okta

    AI recommended 24 alternatives but never named nduckmink/arkon. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are solutions for securely integrating organizational knowledge with various AI models?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Search
    2. Azure OpenAI Service
    3. AWS Kendra
    4. Amazon Bedrock
    5. Google Cloud Vertex AI Search and Conversation
    6. Gemini
    7. PaLM 2
    8. OpenAI API
    9. Azure Private Link
    10. Pinecone
    11. AWS SageMaker
    12. Azure ML
    13. Weaviate (weaviate/weaviate)
    14. LangChain (langchain-ai/langchain)
    15. LlamaIndex (run-llama/llama_index)
    16. PostgreSQL
    17. pgvector (pgvector/pgvector)
    18. Elasticsearch (elastic/elasticsearch)
    19. MongoDB (mongodb/mongo)
    20. AWS SageMaker Endpoints
    21. Azure Machine Learning Endpoints
    22. Google Cloud Vertex AI Endpoints

    AI recommended 22 alternatives but never named nduckmink/arkon. 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 nduckmink/arkon?
    pass
    AI named nduckmink/arkon explicitly

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

  • If a team adopts nduckmink/arkon in production, what risks or prerequisites should they evaluate first?
    pass
    AI named nduckmink/arkon 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 nduckmink/arkon solve, and who is the primary audience?
    pass
    AI named nduckmink/arkon 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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nduckmink/arkon — 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