RRepoGEO

REPOGEO REPORT · LITE

lishuangqiang/AI-Meeting

Default branch main · commit 15000ee6 · scanned 6/28/2026, 4:11:58 PM

GitHub: 513 stars · 74 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 lishuangqiang/AI-Meeting, 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 opening to clarify core purpose and counter AI misinterpretation

    Why:

    CURRENT
    这是一个基于 Spring Boot 3 + Java 17 + Spring AI + MySQL + MongoDB + Redis 构建的 AI 智能助手后端项目，聚焦 AI 对话、智能体会话、模拟面试、实时语音转写和长文本语音合成等场景。项目采用模块化单体架构，支持 HTTP、SSE、WebSocket 多链路交互，兼顾业务完整性、工程规范性和开箱即用性。
    COPY-PASTE FIX
    本项目 `AI-Meeting` 是一个基于 Spring Boot 3 + Java 17 + Spring AI 的 **AI 模拟面试平台后端** 和 **智能对话助手系统**。它专注于简历分析、多轮模拟面试、实时语音转写与合成等核心功能，而非通用会议记录。项目采用模块化单体架构，支持 HTTP、SSE、WebSocket 多链路交互，兼顾业务完整性、工程规范性和开箱即用性。
  • hightopics#2
    Add domain-specific topics to improve category visibility

    Why:

    CURRENT
    java17, mongodb, mysql, redis, spring-boot, springai, sse, websocket
    COPY-PASTE FIX
    java17, mongodb, mysql, redis, spring-boot, springai, sse, websocket, mock-interview, ai-assistant, llm-application, speech-to-text, text-to-speech, resume-analysis, java-ai
  • mediumhomepage#3
    Add a homepage link to the Bilibili demo video

    Why:

    COPY-PASTE FIX
    https://www.bilibili.com/video/BV1ccR9B9EEm/?share_source=copy_web&vd_source=2147a1677cc5a940112d07c6f03c4bc9

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 lishuangqiang/AI-Meeting
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Spring Boot
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Spring Boot · recommended 1×
  2. OpenAI API · recommended 1×
  3. Google Cloud Vertex AI · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Google Cloud Speech-to-Text · recommended 1×
  • CATEGORY QUERY
    How to build an AI-powered mock interview system using Spring Boot and large language models?
    you: not recommended
    AI recommended (in order):
    1. Spring Boot
    2. OpenAI API
    3. Google Cloud Vertex AI
    4. Hugging Face Transformers
    5. Google Cloud Speech-to-Text
    6. OpenAI Whisper
    7. AWS Transcribe
    8. Google Cloud Text-to-Speech
    9. AWS Polly
    10. OpenAI TTS
    11. PostgreSQL
    12. MongoDB
    13. React
    14. Angular
    15. Vue.js

    AI recommended 15 alternatives but never named lishuangqiang/AI-Meeting. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable building real-time AI assistants with speech synthesis and transcription in Java?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Speech-to-Text API
    2. Google Cloud Text-to-Speech API
    3. gRPC
    4. Google Cloud Client Libraries for Java
    5. Amazon Transcribe
    6. Amazon Polly
    7. AWS SDK for Java
    8. Microsoft Azure Cognitive Services Speech SDK for Java
    9. DeepSpeech (Mozilla)
    10. Java Native Access (JNA)
    11. MaryTTS
    12. Vosk (Alpha Cephei)
    13. Picovoice Rhino Speech-to-Intent
    14. Picovoice Porcupine Wake Word

    AI recommended 14 alternatives but never named lishuangqiang/AI-Meeting. 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 lishuangqiang/AI-Meeting?
    pass
    AI named lishuangqiang/AI-Meeting explicitly

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

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