RRepoGEO

REPOGEO REPORT · LITE

plexe-ai/plexe

Default branch main · commit a1e05f6d · scanned 6/19/2026, 8:32:22 PM

GitHub: 2,584 stars · 256 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 plexe-ai/plexe, 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 the README's core purpose statement to prevent miscategorization

    Why:

    CURRENT
    Build machine learning models using natural language.

**plexe** lets you create machine learning models by describing them in plain language. Simply explain what you want, provide a dataset, and the AI-powered system builds a fully functional model through an automated agentic approach.
    COPY-PASTE FIX
    plexe: Generative AutoML – Build machine learning models from natural language prompts.

**plexe** is a Generative AutoML framework that lets you create machine learning models by describing them in plain language. It functions as an automated, agentic system to build fully functional machine learning models from your intent and dataset, similar to platforms like H2O.ai Driverless AI or Google Cloud AutoML, but driven by natural language.
  • hightopics#2
    Add more specific topics to improve category visibility

    Why:

    CURRENT
    agentic-ai, agents, ai, machine-learning, ml, mlengineering, mlops, multiagent
    COPY-PASTE FIX
    agentic-ai, agents, ai, machine-learning, ml, mlengineering, mlops, multiagent, automl, natural-language-to-ml, generative-ai-for-ml, tabular-data-ml
  • mediumabout#3
    Refine the repository description for clarity and keyword density

    Why:

    CURRENT
    ✨ Build a machine learning model from a prompt
    COPY-PASTE FIX
    ✨ Generative AutoML: Build machine learning models from natural language prompts using an agentic approach.

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 plexe-ai/plexe
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
H2O.ai Driverless AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. H2O.ai Driverless AI · recommended 2×
  2. Google Cloud AutoML · recommended 1×
  3. DataRobot · recommended 1×
  4. Microsoft Azure Machine Learning · recommended 1×
  5. Ludwig · recommended 1×
  • CATEGORY QUERY
    Need a tool to generate machine learning models from natural language descriptions and datasets.
    you: not recommended
    AI recommended (in order):
    1. H2O.ai Driverless AI
    2. Google Cloud AutoML
    3. DataRobot
    4. Microsoft Azure Machine Learning
    5. Ludwig
    6. AutoGluon

    AI recommended 6 alternatives but never named plexe-ai/plexe. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an automated system to build ML models using an agentic approach for tabular data.
    you: not recommended
    AI recommended (in order):
    1. AutoGluon (awslabs/autogluon)
    2. H2O.ai Driverless AI
    3. TPOT (EpistasisLab/tpot)
    4. Ludwig (ludwig-ai/ludwig)
    5. Google Cloud AutoML Tables

    AI recommended 5 alternatives but never named plexe-ai/plexe. 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 plexe-ai/plexe?
    pass
    AI named plexe-ai/plexe explicitly

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

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

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

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plexe-ai/plexe — 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