RRepoGEO

REPOGEO REPORT · LITE

mostly-ai/mostlyai

Default branch main · commit 2b061e6a · scanned 6/2/2026, 9:06:59 PM

GitHub: 777 stars · 64 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 mostly-ai/mostlyai, 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
  • highabout#1
    Reposition the 'About' description for clarity

    Why:

    CURRENT
    Synthetic Data SDK ✨
    COPY-PASTE FIX
    Official Python SDK for generating high-fidelity, privacy-preserving synthetic data for enterprise applications.
  • highreadme#2
    Strengthen the README's opening statement

    Why:

    CURRENT
    The **Synthetic Data SDK** is a Python toolkit for high-fidelity, privacy-safe **Synthetic Data**.
    COPY-PASTE FIX
    The **mostly-ai Synthetic Data SDK** is the official Python toolkit for generating high-fidelity, privacy-safe synthetic data, specifically designed for enterprise-grade applications. It empowers data scientists and developers to programmatically create, browse, and manage synthetic data assets with unparalleled statistical accuracy and privacy guarantees.
  • mediumcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why mostly-ai? (Comparison to Alternatives)' or similar, detailing how `mostly-ai/mostlyai` differentiates itself from other synthetic data libraries like SDV, CTGAN, and synthcity, focusing on its enterprise-grade features, data fidelity, and privacy guarantees.

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 mostly-ai/mostlyai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SDV (Synthetic Data Vault)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. SDV (Synthetic Data Vault) · recommended 1×
  2. CTGAN (Conditional Tabular GAN) · recommended 1×
  3. synthcity · recommended 1×
  4. Pydp (Google's Differential Privacy Library) · recommended 1×
  5. SmartNoise (OpenDP) · recommended 1×
  • CATEGORY QUERY
    What Python libraries help generate privacy-preserving synthetic data for machine learning?
    you: not recommended
    AI recommended (in order):
    1. SDV (Synthetic Data Vault)
    2. CTGAN (Conditional Tabular GAN)
    3. synthcity
    4. Pydp (Google's Differential Privacy Library)
    5. SmartNoise (OpenDP)
    6. Synthetic Data Generation (SDG) by Gretel.ai

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

    Show full AI answer
  • CATEGORY QUERY
    How to create realistic synthetic datasets from existing tabular data using generative models?
    you: not recommended
    AI recommended (in order):
    1. CTGAN
    2. TVAE
    3. Synthesizer
    4. SDV
    5. CopulaGAN
    6. diffusers

    AI recommended 6 alternatives but never named mostly-ai/mostlyai. 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 mostly-ai/mostlyai?
    pass
    AI named mostly-ai/mostlyai explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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