RRepoGEO

REPOGEO REPORT · LITE

dzhng/deep-seek

Default branch main · commit 1a30f130 · scanned 6/7/2026, 10:03:09 PM

GitHub: 513 stars · 54 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 dzhng/deep-seek, 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 disambiguation note to the README

    Why:

    COPY-PASTE FIX
    Add a prominent note near the top of the README, e.g., "Note: This project is an independent 'retrieval engine' architecture and is not affiliated with or based on the DeepSeek LLM family (e.g., DeepSeek Coder)."
  • mediumreadme#2
    Refine README opening to highlight unique value proposition

    Why:

    CURRENT
    This is a new experimental architecture for a llm powered internet scale _retrieval engine_. This architecture is very different from current research agents, which are designed as _answer engines_.
    COPY-PASTE FIX
    This is a new experimental architecture for an LLM-powered, internet-scale **retrieval engine** designed to collect comprehensive lists of entities from vast sources. Unlike typical 'answer engines' (e.g., Perplexity, gpt-researcher) that aim for a single correct answer, DeepSeek focuses on exhaustive data collection and enrichment, producing detailed tables of retrieved entities.
  • lowtopics#3
    Add more specific topics related to large-scale data processing and entity extraction

    Why:

    CURRENT
    agent, agentic, ai, anthropic, data-retrieval, knowledge-graph, llm, openai, research-age, search
    COPY-PASTE FIX
    agent, agentic, ai, anthropic, data-retrieval, knowledge-graph, llm, openai, research-agent, search, entity-extraction, large-scale-data, web-scraping, information-extraction

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 dzhng/deep-seek
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
https://github.com/explosion/spaCy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. https://github.com/explosion/spaCy · recommended 1×
  2. https://github.com/huggingface/transformers · recommended 1×
  3. Google Cloud Natural Language API · recommended 1×
  4. Amazon Comprehend · recommended 1×
  5. Microsoft Azure AI Language · recommended 1×
  • CATEGORY QUERY
    What AI tools help collect comprehensive entity lists from many online documents?
    you: not recommended
    AI recommended (in order):
    1. spaCy (https://github.com/explosion/spaCy)
    2. Hugging Face Transformers (https://github.com/huggingface/transformers)
    3. Google Cloud Natural Language API
    4. Amazon Comprehend
    5. Microsoft Azure AI Language
    6. OpenAI GPT-3.5 / GPT-4
    7. Stanford CoreNLP (https://github.com/stanfordnlp/CoreNLP)

    AI recommended 7 alternatives but never named dzhng/deep-seek. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an LLM-powered system to perform deep data retrieval, not just answer questions.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Weaviate
    5. Pinecone
    6. Elasticsearch

    AI recommended 6 alternatives but never named dzhng/deep-seek. 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 dzhng/deep-seek?
    pass
    AI named dzhng/deep-seek explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite