RRepoGEO

REPOGEO REPORT · LITE

mckinsey/vizro

Default branch main · commit 4dd87502 · scanned 5/22/2026, 1:16:19 PM

GitHub: 3,693 stars · 273 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 mckinsey/vizro, 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
    Strengthen the opening paragraph of the 'What is Vizro?' section

    Why:

    CURRENT
    Vizro is an open-source Python-based toolkit. Use it to build beautiful and powerful data visualization apps quickly and easily, without needing advanced engineering or visual design expertise.
    COPY-PASTE FIX
    Vizro is an open-source, low-code Python toolkit designed for building powerful, interactive data visualization dashboards and multi-page web applications quickly and easily, without needing advanced engineering or visual design expertise.
  • mediumtopics#2
    Expand repository topics with relevant keywords

    Why:

    CURRENT
    dashboard, data-visualization, plotly, plotly-dash, pydantic, python, visualization
    COPY-PASTE FIX
    dashboard, data-visualization, plotly, plotly-dash, pydantic, python, visualization, low-code, interactive-dashboards, web-app, data-apps, dashboard-builder
  • lowreadme#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

Vizro stands out from tools like Streamlit, Panel, and Dash by offering a declarative, low-code approach to building production-ready data visualization applications. While other frameworks provide flexibility, Vizro focuses on accelerating development with built-in design best practices and a configuration-driven structure, making it ideal for data scientists and analysts who need to quickly deploy high-quality, multi-page dashboards without extensive web development expertise.

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 mckinsey/vizro
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
streamlit/streamlit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. streamlit/streamlit · recommended 1×
  2. holoviz/panel · recommended 1×
  3. plotly/dash · recommended 1×
  4. voila-dashboards/voila · recommended 1×
  5. gradio-app/gradio · recommended 1×
  • CATEGORY QUERY
    How to quickly build interactive data visualization dashboards using Python with minimal code?
    you: not recommended
    AI recommended (in order):
    1. Streamlit (streamlit/streamlit)
    2. Panel (holoviz/panel)
    3. Dash (plotly/dash)
    4. Voila (voila-dashboards/voila)
    5. Gradio (gradio-app/gradio)

    AI recommended 5 alternatives but never named mckinsey/vizro. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best low-code Python tools for creating powerful data visualization applications easily?
    you: not recommended
    AI recommended (in order):
    1. Streamlit
    2. Panel
    3. Dash
    4. Gradio
    5. Voilà
    6. PyWebIO

    AI recommended 6 alternatives but never named mckinsey/vizro. 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 mckinsey/vizro?
    pass
    AI named mckinsey/vizro explicitly

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

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

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

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mckinsey/vizro — 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