RRepoGEO

REPOGEO REPORT · LITE

thu-vu92/local-llms-analyse-finance

Default branch main · commit a443f605 · scanned 5/31/2026, 10:27:38 AM

GitHub: 867 stars · 244 forks

AI VISIBILITY SCORE
22 /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
1 / 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 thu-vu92/local-llms-analyse-finance, 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
  • hightopics#1
    Add specific topics to the repository

    Why:

    COPY-PASTE FIX
    local-llm, llama2, finance, bank-transactions, data-labeling, ollama, personal-finance, ai-application
  • highreadme#2
    Reposition the README's opening paragraph to emphasize specific application

    Why:

    CURRENT
    In this project, I explored how local LLMs can be used to label data and support analyses. Specifically, I used Llama2 model to automatically categorise my bank transaction data.
    COPY-PASTE FIX
    This project demonstrates a practical application of local Large Language Models (LLMs) to automatically categorize personal bank transaction data, offering a privacy-preserving solution for financial analysis using models like Llama2.
  • mediumhomepage#3
    Add a homepage link to the project's About section

    Why:

    COPY-PASTE FIX
    https://www.youtube.com/watch?v=h_GTxRFYETY

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 thu-vu92/local-llms-analyse-finance
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Python
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Python · recommended 1×
  2. scikit-learn/scikit-learn · recommended 1×
  3. pandas-dev/pandas · recommended 1×
  4. nltk/nltk · recommended 1×
  5. explosion/spaCy · recommended 1×
  • CATEGORY QUERY
    How can I automatically categorize my bank transactions using a locally deployed AI?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. scikit-learn (scikit-learn/scikit-learn)
    3. Pandas (pandas-dev/pandas)
    4. NLTK (nltk/nltk)
    5. spaCy (explosion/spaCy)
    6. Jupyter Notebook (jupyter/notebook)
    7. JupyterLab (jupyterlab/jupyterlab)
    8. SQLite
    9. FastAPI (tiangolo/fastapi)
    10. Flask (pallets/flask)
    11. Docker (docker/docker-ce)

    AI recommended 11 alternatives but never named thu-vu92/local-llms-analyse-finance. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a solution to label financial data using open-source large language models offline.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. BloombergGPT
    3. FinBERT
    4. RoBERTa-base-finetuned-finance
    5. DistilBERT
    6. TinyBERT
    7. spaCy
    8. OpenNMT
    9. Fairseq
    10. Stanford CoreNLP
    11. Gensim

    AI recommended 11 alternatives but never named thu-vu92/local-llms-analyse-finance. 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 thu-vu92/local-llms-analyse-finance?
    pass
    AI did not name thu-vu92/local-llms-analyse-finance — 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 thu-vu92/local-llms-analyse-finance in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thu-vu92/local-llms-analyse-finance 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 thu-vu92/local-llms-analyse-finance solve, and who is the primary audience?
    pass
    AI did not name thu-vu92/local-llms-analyse-finance — 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?

Embed your GEO score

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thu-vu92/local-llms-analyse-finance — 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