RRepoGEO

REPOGEO REPORT · LITE

Azure/GPT-RAG

Default branch main · commit a4ab9d19 · scanned 6/19/2026, 1:42:05 PM

GitHub: 1,161 stars · 304 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 Azure/GPT-RAG, 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
  • hightopics#1
    Update repository topics to reflect enterprise, secure RAG focus

    Why:

    CURRENT
    azd-templates, azure, gpt-3, gpt-4, openai
    COPY-PASTE FIX
    azure-openai, rag, enterprise-ai, solution-accelerator, zero-trust, network-isolation, reference-architecture, azd-templates
  • highreadme#2
    Strengthen the README's opening statement to emphasize unique value proposition

    Why:

    CURRENT
    # GPT-RAG Solution Accelerator
This solution accelerator provides architecture templates and deployment assets to help organizations build secure, scalable, and enterprise-ready **Retrieval-Augmented Generation (RAG)** solutions powered by **AI Agents**.
    COPY-PASTE FIX
    # GPT-RAG: Enterprise Solution Accelerator for Secure, Network-Isolated RAG on Azure
This solution accelerator provides architecture templates and deployment assets to help organizations build secure, scalable, and enterprise-ready **Retrieval-Augmented Generation (RAG)** solutions powered by **AI Agents** on Azure. It focuses on **Zero-Trust security** and **network-isolated deployments**, offering a robust reference architecture for operationalizing Generative AI with confidence.
  • mediumabout#3
    Refine the repository description for conciseness and keyword density

    Why:

    CURRENT
    Sharing the learning along the way we been gathering to enable Azure OpenAI at enterprise scale in a secure manner. GPT-RAG core is a Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
    COPY-PASTE FIX
    A solution accelerator and reference architecture for building secure, scalable, and enterprise-ready Retrieval-Augmented Generation (RAG) solutions on Azure. It leverages Azure OpenAI and Azure Cognitive Search, focusing on Zero-Trust security and network-isolated deployments for production-grade AI applications.

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/GPT-RAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 4×
  2. LangChain · recommended 3×
  3. OpenAI API · recommended 3×
  4. Azure OpenAI Service · recommended 2×
  5. Hugging Face Inference API · recommended 2×
  • CATEGORY QUERY
    How to build a secure, scalable RAG solution for enterprise applications?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Search
    2. Azure OpenAI Service
    3. AWS Kendra
    4. Amazon Bedrock
    5. Amazon SageMaker
    6. Elasticsearch
    7. LangChain
    8. LlamaIndex
    9. OpenAI API
    10. Hugging Face Inference API
    11. Pinecone
    12. Weaviate
    13. Qdrant
    14. LangChain
    15. LlamaIndex
    16. OpenAI API
    17. Hugging Face Inference API
    18. Google Cloud Vertex AI Search
    19. Vertex AI PaLM API
    20. Vertex AI Gemini API
    21. Milvus
    22. Faiss
    23. Haystack
    24. LlamaIndex
    25. Llama 2
    26. Mistral
    27. Kubernetes
    28. NVIDIA Triton Inference Server
    29. vLLM

    AI recommended 29 alternatives but never named Azure/GPT-RAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Frameworks for secure, network-isolated retrieval augmented generation deployments?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NeMo Guardrails
    2. LangChain
    3. NVIDIA Triton Inference Server
    4. vLLM
    5. Milvus
    6. Weaviate
    7. Chroma
    8. LlamaIndex
    9. Haystack
    10. deepset
    11. OpenAI API
    12. Azure OpenAI Service
    13. Azure Stack Hub
    14. Azure Stack Edge
    15. Hugging Face Transformers
    16. PyTorch
    17. TensorFlow

    AI recommended 17 alternatives but never named Azure/GPT-RAG. 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 Azure/GPT-RAG?
    pass
    AI named Azure/GPT-RAG 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/GPT-RAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Azure/GPT-RAG 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/GPT-RAG solve, and who is the primary audience?
    pass
    AI named Azure/GPT-RAG explicitly

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

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Azure/GPT-RAG — 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