RRepoGEO

REPOGEO REPORT · LITE

dotnet/eShopSupport

Default branch main · commit 1a772ff8 · scanned 6/13/2026, 2:01:58 PM

GitHub: 650 stars · 185 forks

AI VISIBILITY SCORE
28 /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
2 / 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 dotnet/eShopSupport, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumreadme#1
    Refine README's opening to emphasize its role as an AI solution blueprint

    Why:

    CURRENT
    A sample .NET application showcasing common use cases and development practices for build AI solutions in .NET (Generative AI, specifically).
    COPY-PASTE FIX
    This reference .NET application serves as a comprehensive blueprint and learning resource for building AI solutions, specifically demonstrating generative AI patterns within a customer support context.
  • lowhomepage#2
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/dotnet/eShopSupport

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 dotnet/eShopSupport
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. OpenAI-DotNet · recommended 2×
  3. Hugging Face Transformers · recommended 2×
  4. Azure OpenAI Service · recommended 1×
  5. Azure AI Search · recommended 1×
  • CATEGORY QUERY
    Seeking a sample .NET solution for integrating generative AI into customer support workflows.
    you: not recommended
    AI recommended (in order):
    1. Azure OpenAI Service
    2. Azure AI Search
    3. Azure Bot Service
    4. Azure.AI.OpenAI
    5. Azure.Search.Documents
    6. Bot Framework SDK for C#
    7. OpenAI API
    8. OpenAI-DotNet
    9. Pinecone
    10. Weaviate
    11. Qdrant
    12. ASP.NET Core
    13. Semantic Kernel
    14. Microsoft.SemanticKernel
    15. LangChain.NET
    16. Microsoft Copilot Studio
    17. Power Virtual Agents
    18. Hugging Face Transformers
    19. SciSharp.TensorFlow.Keras
    20. TorchSharp
    21. TensorFlow
    22. PyTorch

    AI recommended 22 alternatives but never named dotnet/eShopSupport. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement AI features like text classification and summarization in a .NET application?
    you: not recommended
    AI recommended (in order):
    1. Azure Cognitive Services (Text Analytics API)
    2. ML.NET
    3. Hugging Face Transformers
    4. ONNX Runtime
    5. Python.NET
    6. OpenAI API
    7. OpenAI-DotNet
    8. Google Cloud AI (Natural Language API)
    9. AWS Comprehend

    AI recommended 9 alternatives but never named dotnet/eShopSupport. 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 dotnet/eShopSupport?
    pass
    AI did not name dotnet/eShopSupport — likely talking about a different project

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

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

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

Embed your GEO score

Drop this badge into the README of dotnet/eShopSupport. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/dotnet/eShopSupport.svg)](https://repogeo.com/en/r/dotnet/eShopSupport)
HTML
<a href="https://repogeo.com/en/r/dotnet/eShopSupport"><img src="https://repogeo.com/badge/dotnet/eShopSupport.svg" alt="RepoGEO" /></a>
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dotnet/eShopSupport — 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