RRepoGEO

REPOGEO REPORT · LITE

LLMQuant/quant-mind

Default branch master · commit 8e218884 · scanned 6/14/2026, 7:47:41 PM

GitHub: 1,369 stars · 221 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 LLMQuant/quant-mind, 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
    Explicitly clarify the project's core purpose and disambiguate from LLM quantization

    Why:

    CURRENT
    QuantMind** is an intelligent knowledge extraction and retrieval framework for quantitative finance. It transforms unstructured financial content—papers, news, blogs, reports—into a queryable knowledge base, enabling AI-powered research at scale.
    COPY-PASTE FIX
    **QuantMind: Your AI-Powered Knowledge Engine for Quantitative Finance.** This framework specializes in extracting and retrieving insights from unstructured financial data—papers, news, blogs, and reports—to build a queryable knowledge base for advanced quantitative research. **It is not an LLM model quantization library.**
  • mediumtopics#2
    Add more specific topics related to financial AI and RAG

    Why:

    CURRENT
    data, knowledge, llm, pipeline, quantitative-finance, quantitative-research, workflow
    COPY-PASTE FIX
    data, knowledge, llm, pipeline, quantitative-finance, quantitative-research, workflow, financial-ai, rag, knowledge-graph, information-extraction, nlp, finance, investment
  • lowreadme#3
    Enhance the 'Why QuantMind' section with explicit differentiators

    Why:

    COPY-PASTE FIX
    In the 'Why QuantMind' section, add: 'Unlike generic RAG frameworks, QuantMind is purpose-built for the complexities of quantitative finance, offering specialized extraction and structuring of financial content. Compared to traditional data terminals, it provides an open, AI-driven framework for custom research and knowledge base creation.'

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 LLMQuant/quant-mind
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Bloomberg Terminal
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Bloomberg Terminal · recommended 1×
  2. Refinitiv Eikon · recommended 1×
  3. FactSet · recommended 1×
  4. crummy/BeautifulSoup · recommended 1×
  5. scrapy/scrapy · recommended 1×
  • CATEGORY QUERY
    How to build an AI-powered research system for quantitative finance using unstructured data?
    you: not recommended
    AI recommended (in order):
    1. Bloomberg Terminal
    2. Refinitiv Eikon
    3. FactSet
    4. Beautiful Soup (crummy/BeautifulSoup)
    5. Scrapy (scrapy/scrapy)
    6. spaCy (explosion/spaCy)
    7. Hugging Face Transformers (huggingface/transformers)
    8. Gensim (RaRe-Technologies/gensim)
    9. NLTK (nltk/nltk)
    10. scikit-learn (scikit-learn/scikit-learn)
    11. PyTorch (pytorch/pytorch)
    12. TensorFlow (tensorflow/tensorflow)
    13. XGBoost (dmlc/xgboost)
    14. LightGBM (microsoft/LightGBM)
    15. PostgreSQL
    16. MySQL
    17. MongoDB
    18. Apache Cassandra (apache/cassandra)
    19. Amazon S3
    20. Google Cloud Storage
    21. Azure Blob Storage
    22. Apache Airflow (apache/airflow)
    23. Docker (moby/moby)
    24. Kubernetes (kubernetes/kubernetes)

    AI recommended 24 alternatives but never named LLMQuant/quant-mind. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework can extract and retrieve knowledge from financial documents for quantitative analysis?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. SpaCy
    5. NLTK

    AI recommended 5 alternatives but never named LLMQuant/quant-mind. 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 LLMQuant/quant-mind?
    pass
    AI named LLMQuant/quant-mind explicitly

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

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

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

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LLMQuant/quant-mind — 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