RRepoGEO

REPOGEO REPORT · LITE

aws-samples/swift-chat

Default branch main · commit 555be944 · scanned 6/17/2026, 9:47:46 AM

GitHub: 826 stars · 119 forks

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 aws-samples/swift-chat, 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
    Add a prominent disclaimer in README about the React Native tech stack

    Why:

    CURRENT
    # Sample Vif Chat App — A Cross-platform AI Assistant

> 🚀 Your Personal AI Workspace — Chat, Create Apps, and More
    COPY-PASTE FIX
    # Sample Vif Chat App — A Cross-platform AI Assistant

**Note:** Despite the repository name `swift-chat`, this project is built with React Native, not Swift.

> 🚀 Your Personal AI Workspace — Chat, Create Apps, and More
  • highabout#2
    Refine the 'About' description to emphasize 'sample app'

    Why:

    CURRENT
    A lightning-fast, cross-platform AI Assistant App built with React Native.
    COPY-PASTE FIX
    A lightning-fast, cross-platform **sample** AI Assistant App built with React Native, demonstrating full-stack integration with AWS.
  • hightopics#3
    Add more general topics to reinforce 'AI Assistant' and 'Sample Application'

    Why:

    CURRENT
    amazon-bedrock, amazon-nova, android, aws, chat, claude-4-sonnet, deepseek, ios, mac, mobile-app, ollama, react-native, sample-vif-chat-app, speech-to-speech, vif-chat, vifchat, virtual-try-on
    COPY-PASTE FIX
    ["amazon-bedrock", "amazon-nova", "android", "aws", "chat", "claude-4-sonnet", "deepseek", "ios", "mac", "mobile-app", "ollama", "react-native", "sample-vif-chat-app", "speech-to-speech", "vif-chat", "vifchat", "virtual-try-on", "ai-assistant", "sample-application", "fullstack-app"]

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 aws-samples/swift-chat
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud AI Platform
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud AI Platform · recommended 1×
  2. Dialogflow ES · recommended 1×
  3. Dialogflow CX · recommended 1×
  4. Speech-to-Text · recommended 1×
  5. Text-to-Speech · recommended 1×
  • CATEGORY QUERY
    How to build a cross-platform AI assistant application using React Native?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud AI Platform
    2. Dialogflow ES
    3. Dialogflow CX
    4. Speech-to-Text
    5. Text-to-Speech

    AI recommended 5 alternatives but never named aws-samples/swift-chat. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable building an AI chat app with voice, image, and multiple LLM backends?
    you: not recommended
    AI recommended (in order):
    1. Gradio
    2. Streamlit
    3. LangChain
    4. OpenAI API
    5. Google Gemini API
    6. Anthropic Claude API
    7. AssemblyAI
    8. Google Cloud Speech-to-Text
    9. OpenAI Whisper
    10. Pillow
    11. OpenCV-Python
    12. FastAPI
    13. Flask

    AI recommended 13 alternatives but never named aws-samples/swift-chat. 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 aws-samples/swift-chat?
    pass
    AI named aws-samples/swift-chat explicitly

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

  • If a team adopts aws-samples/swift-chat in production, what risks or prerequisites should they evaluate first?
    pass
    AI named aws-samples/swift-chat 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 aws-samples/swift-chat solve, and who is the primary audience?
    pass
    AI named aws-samples/swift-chat 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 aws-samples/swift-chat. 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)
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HTML
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