RRepoGEO

REPOGEO REPORT · LITE

Azure/GPT-RAG

Default branch main · commit f861c09f · scanned 5/9/2026, 4:47:05 PM

GitHub: 1,149 stars · 299 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 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
  • highreadme#1
    Reposition README's opening to emphasize 'prescriptive reference architecture'

    Why:

    CURRENT
    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
    This solution accelerator provides a prescriptive, end-to-end reference architecture and deployment assets to help organizations build secure, scalable, and enterprise-ready **Retrieval-Augmented Generation (RAG)** solutions powered by **AI Agents**.
  • mediumtopics#2
    Add more specific topics to clarify the repo's solution type

    Why:

    CURRENT
    azd-templates, azure, gpt-3, gpt-4, openai
    COPY-PASTE FIX
    azd-templates, azure, gpt-3, gpt-4, openai, rag-architecture, enterprise-ai, solution-accelerator, reference-architecture, zero-trust-ai
  • lowabout#3
    Slightly refine the description to reinforce 'solution accelerator' and 'reference architecture'

    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
    This repository provides a prescriptive, enterprise-scale Retrieval-Augmented Generation (RAG) solution accelerator and reference architecture for Azure OpenAI. It enables secure, scalable RAG patterns using Azure Cognitive Search and Azure OpenAI for ChatGPT-style and Q&A experiences.

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
Azure OpenAI Service
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure OpenAI Service · recommended 2×
  2. Azure AI Search · recommended 2×
  3. AWS Bedrock · recommended 2×
  4. Amazon OpenSearch Service · recommended 2×
  5. Google Cloud Vertex AI · recommended 2×
  • CATEGORY QUERY
    How to build secure enterprise-grade RAG solutions for custom data using large language models?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Studio
    2. Azure Machine Learning
    3. Azure OpenAI Service
    4. Azure AI Search
    5. AWS Bedrock
    6. Amazon Kendra
    7. Amazon OpenSearch Service
    8. Google Cloud Vertex AI
    9. Vertex AI Search and Conversation
    10. Hugging Face Transformers
    11. Hugging Face Inference Endpoints
    12. LangChain
    13. LlamaIndex
    14. Pinecone
    15. Weaviate
    16. Milvus
    17. Databricks Lakehouse AI
    18. Databricks Vector Search
    19. Databricks Model Serving
    20. AWS KMS
    21. Azure Key Vault
    22. Google Cloud KMS
    23. Azure AI Content Safety
    24. AWS Comprehend
    25. Unity Catalog

    AI recommended 25 alternatives but never named Azure/GPT-RAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are robust cloud architecture patterns for AI agent-powered Q&A over proprietary documents?
    you: not recommended
    AI recommended (in order):
    1. Azure OpenAI Service
    2. Azure AI Search
    3. Azure Cosmos DB
    4. Azure Functions
    5. Azure App Service
    6. AWS Bedrock
    7. Amazon OpenSearch Service
    8. Amazon S3
    9. Amazon DynamoDB
    10. AWS Lambda
    11. Amazon ECS
    12. Fargate
    13. Google Cloud Vertex AI
    14. Google Cloud Search
    15. Google Cloud Storage
    16. Firestore
    17. Cloud Functions
    18. Cloud Run
    19. Llama 2
    20. Mistral
    21. Falcon
    22. Hugging Face Transformers
    23. vLLM
    24. Elasticsearch
    25. OpenSearch
    26. MinIO
    27. PostgreSQL
    28. pgvector
    29. Kubernetes
    30. Docker
    31. Pinecone
    32. Weaviate
    33. Milvus
    34. OpenAI API
    35. Anthropic API

    AI recommended 35 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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  • Brand-free category queries5 vs 2 in Lite
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