RRepoGEO

REPOGEO REPORT · LITE

a16z-infra/companion-app

Default branch main · commit a50c9371 · scanned 5/15/2026, 3:03:27 PM

GitHub: 5,954 stars · 962 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 a16z-infra/companion-app, 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
  • highreadme#1
    Reposition the README H1 to emphasize 'starter kit' and 'stack'

    Why:

    CURRENT
    # AI Companion App (based on AI Getting Started template)
    COPY-PASTE FIX
    # AI Companion Stack: A Starter Kit for Building AI Companions with Memory
  • mediumreadme#2
    Integrate the 'tutorial/starter stack' purpose into the README's first paragraph

    Why:

    CURRENT
    This is a tutorial stack to create and host AI companions that you can chat with on a browser or text via SMS. It allows you to determine the personality and backstory of your companion, and uses a vector database with similarity search to retrieve and prompt so the conversations have more depth. It also provides some conversational memory by keeping the conversation in a queue and including it in the prompt.
    COPY-PASTE FIX
    This project is a lightweight, full-stack tutorial and starter kit designed for engineers to create and host their own AI companions. It provides a reference architecture to accelerate the development of AI-powered products, demonstrating how to build companions with customizable personalities, conversational memory, and vector database integration for deeper interactions. While intended as a developer tutorial, it serves as a robust foundation for those curious about building AI chatbots.

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 a16z-infra/companion-app
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. LangChain · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Llama 2 · recommended 2×
  5. Pinecone · recommended 2×
  • CATEGORY QUERY
    How can I build an AI companion with customizable personality and conversational memory?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. GPT-3.5
    3. GPT-4
    4. LangChain
    5. LlamaIndex
    6. Hugging Face Transformers
    7. GPT-2
    8. DialoGPT
    9. Llama 2
    10. Pinecone
    11. Weaviate
    12. ChromaDB
    13. Google Cloud's Dialogflow CX
    14. Rasa
    15. Microsoft Azure AI Bot Service
    16. Azure OpenAI Service

    AI recommended 16 alternatives but never named a16z-infra/companion-app. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good starter kits for developing personalized AI chat agents with memory?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI API
    3. Pinecone
    4. ChromaDB
    5. Weaviate
    6. Anthropic's Claude
    7. Google's Gemini
    8. Llama 2
    9. LlamaIndex
    10. Botpress
    11. Rasa
    12. Voiceflow

    AI recommended 12 alternatives but never named a16z-infra/companion-app. 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 a16z-infra/companion-app?
    pass
    AI named a16z-infra/companion-app explicitly

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

  • If a team adopts a16z-infra/companion-app in production, what risks or prerequisites should they evaluate first?
    pass
    AI named a16z-infra/companion-app 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 a16z-infra/companion-app solve, and who is the primary audience?
    pass
    AI named a16z-infra/companion-app explicitly

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

Embed your GEO score

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MARKDOWN (README)
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HTML
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a16z-infra/companion-app — 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