RRepoGEO

REPOGEO REPORT · LITE

Azure/AI-in-a-Box

Default branch main · commit 52ff38a2 · scanned 6/10/2026, 11:37:05 AM

GitHub: 600 stars · 195 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
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 Azure/AI-in-a-Box, 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 opening paragraph to emphasize deployable reference architectures

    Why:

    CURRENT
    AI-in-a-Box leverages the collective expertise of Microsoft Customer Engineers and Architects across the globe to develop and provide AI and ML solutions to the technical community. Our intent is to present a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency.
    COPY-PASTE FIX
    AI-in-a-Box is a curated collection of **deployable solution accelerators and reference architectures** from Microsoft experts, designed to help engineers rapidly establish and deploy their AI/ML environments and applications with minimal friction. We provide proven templates for common AI scenarios like chatbots, custom vision, and document intelligence, leveraging Azure services.
  • mediumtopics#2
    Add specific topics for solution accelerators and reference architectures

    Why:

    CURRENT
    ai, azd, azd-templates, azure, chat-bot, chatbot, chatgpt, custom-vision, document-intelligence, edge-ai, edge-computing, langchain, machine-learning, openai, semantic-kernel
    COPY-PASTE FIX
    ai, azd, azd-templates, azure, chat-bot, chatbot, chatgpt, custom-vision, document-intelligence, edge-ai, edge-computing, langchain, machine-learning, openai, semantic-kernel, solution-accelerator, reference-architecture, ai-templates, deployment-templates
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the official project homepage URL here (e.g., a dedicated project page, documentation site, or relevant Microsoft Learn page).

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 Azure/AI-in-a-Box
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Spaces
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Spaces · recommended 1×
  2. Google Cloud Vertex AI Workbench · recommended 1×
  3. Google Colaboratory · recommended 1×
  4. AWS SageMaker Studio Lab · recommended 1×
  5. Amazon SageMaker JumpStart · recommended 1×
  • CATEGORY QUERY
    How can I rapidly deploy pre-built AI/ML solutions and environments with minimal friction?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Spaces
    2. Google Cloud Vertex AI Workbench
    3. Google Colaboratory
    4. AWS SageMaker Studio Lab
    5. Amazon SageMaker JumpStart
    6. Microsoft Azure Machine Learning
    7. Gradio
    8. Streamlit
    9. Google Cloud Run
    10. AWS App Runner
    11. Vercel
    12. Netlify
    13. Replicate

    AI recommended 13 alternatives but never named Azure/AI-in-a-Box. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find reusable solution accelerators for AI chatbots, custom vision, or document intelligence?
    you: not recommended
    AI recommended (in order):
    1. Azure Bot Service
    2. Azure Custom Vision
    3. Azure AI Document Intelligence
    4. Amazon Lex
    5. Amazon Rekognition
    6. Amazon Textract
    7. Dialogflow
    8. Google Cloud Vision AI
    9. Google Cloud Document AI
    10. Hugging Face Transformers
    11. OpenAI API
    12. UiPath Document Understanding
    13. DataRobot

    AI recommended 13 alternatives but never named Azure/AI-in-a-Box. 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 Azure/AI-in-a-Box?
    pass
    AI named Azure/AI-in-a-Box explicitly

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

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

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

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