RRepoGEO

REPOGEO REPORT · LITE

AgriQuantAI/AgriQuant-AI

Default branch main · commit 6b8a25e7 · scanned 6/22/2026, 10:38:10 PM

GitHub: 716 stars · 26 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 AgriQuantAI/AgriQuant-AI, 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
    Reposition README's opening to emphasize quantitative trading system

    Why:

    CURRENT
    **AI-driven weather intelligence for agricultural commodity futures.**
    COPY-PASTE FIX
    AgriQuant AI is an **AI-powered quantitative trading intelligence system** for agricultural commodity futures, predicting price moves 48-72 hours early across OJ, Coffee, Cocoa, Sugar, Corn and Wheat.
  • mediumlicense#2
    Add a standard open-source LICENSE file

    Why:

    COPY-PASTE FIX
    MIT License

Copyright (c) [YEAR] [FULL NAME]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
  • lowabout#3
    Update GitHub 'About' description for clarity

    Why:

    CURRENT
    AI-powered weather intelligence for agricultural commodity futures. Predicts price moves 48-72 hours early across OJ, Coffee, Cocoa, Sugar, Corn and Wheat using Claude Sonnet + satellite data
    COPY-PASTE FIX
    AI-powered quantitative trading intelligence system for agricultural commodity futures. Predicts price moves 48-72 hours early across OJ, Coffee, Cocoa, Sugar, Corn and Wheat using Claude Sonnet + satellite data.

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 AgriQuantAI/AgriQuant-AI
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Vertex AI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Vertex AI · recommended 1×
  2. Google Earth Engine · recommended 1×
  3. tensorflow/tensorflow · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. Amazon SageMaker · recommended 1×
  • CATEGORY QUERY
    How to predict agricultural commodity futures price movements using AI and satellite weather data?
    you: not recommended
    AI recommended (in order):
    1. Vertex AI
    2. Google Earth Engine
    3. TensorFlow (tensorflow/tensorflow)
    4. PyTorch (pytorch/pytorch)
    5. Amazon SageMaker
    6. AWS Data Exchange
    7. Open Data on AWS
    8. Microsoft Azure Machine Learning
    9. Azure Maps
    10. Microsoft Planetary Computer
    11. Databricks Lakehouse Platform
    12. Delta Lake (delta-io/delta)
    13. MLflow (mlflow/mlflow)
    14. H2O.ai Driverless AI
    15. XGBoost (dmlc/xgboost)
    16. LightGBM (microsoft/LightGBM)
    17. scikit-learn (scikit-learn/scikit-learn)
    18. pandas (pandas-dev/pandas)
    19. numpy (numpy/numpy)
    20. xarray (pydata/xarray)
    21. rasterio (rasterio/rasterio)
    22. Prophet (facebook/prophet)
    23. statsmodels (statsmodels/statsmodels)

    AI recommended 23 alternatives but never named AgriQuantAI/AgriQuant-AI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking AI-driven solutions for quantitative finance to forecast agricultural market trends from weather intelligence.
    you: not recommended
    AI recommended (in order):
    1. Climavision
    2. Tomorrow.io
    3. IBM Environmental Intelligence Suite
    4. Descartes Labs
    5. Gro Intelligence
    6. AccuWeather For Business
    7. DTN

    AI recommended 7 alternatives but never named AgriQuantAI/AgriQuant-AI. 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 AgriQuantAI/AgriQuant-AI?
    pass
    AI named AgriQuantAI/AgriQuant-AI explicitly

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

  • If a team adopts AgriQuantAI/AgriQuant-AI in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AgriQuantAI/AgriQuant-AI 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 AgriQuantAI/AgriQuant-AI solve, and who is the primary audience?
    pass
    AI named AgriQuantAI/AgriQuant-AI 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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  • Brand-free category queries5 vs 2 in Lite
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