RRepoGEO

REPOGEO REPORT · LITE

AprilNEA/AChat

Default branch canary · commit cac4e4a5 · scanned 5/31/2026, 10:21:52 AM

GitHub: 3,259 stars · 1,159 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 AprilNEA/AChat, 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
  • mediumreadme#1
    Add a 'What is AChat?' or 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    ## What is AChat?
AChat is an open-source, self-hosted AI platform designed for enterprises and teams to manage and synchronize AI conversations securely. Unlike generic chat applications or raw LLM frameworks, AChat provides a complete solution for centralized AI dialogue management, team collaboration, and built-in commercial support.
  • mediumreadme#2
    Clarify the project's license(s) directly in the README

    Why:

    COPY-PASTE FIX
    ## License
This project uses a custom license. Please refer to the [LICENSE file](https://github.com/AprilNEA/AChat/blob/main/LICENSE) for full details.

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 AprilNEA/AChat
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
mattermost/mattermost
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. mattermost/mattermost · recommended 1×
  2. OpenAI's GPT series · recommended 1×
  3. Llama 2 · recommended 1×
  4. RocketChat/Rocket.Chat · recommended 1×
  5. open-webui/open-webui · recommended 1×
  • CATEGORY QUERY
    How can my team self-host an AI chat platform for managing conversations securely?
    you: not recommended
    AI recommended (in order):
    1. Mattermost (mattermost/mattermost)
    2. OpenAI's GPT series
    3. Llama 2
    4. Rocket.Chat (RocketChat/Rocket.Chat)
    5. Open-WebUI (open-webui/open-webui)
    6. Oobabooga's Text Generation WebUI (oobabooga/text-generation-webui)
    7. Mistral
    8. Falcon
    9. PrivateGPT (imartinez/privateGPT)
    10. LocalGPT (PromtEngineer/localGPT)
    11. FastAPI (tiangolo/fastapi)
    12. Flask (pallets/flask)
    13. Llama.cpp (ggerganov/llama.cpp)
    14. vLLM (vllm-project/vllm)
    15. Jitsi Meet (jitsi/jitsi-meet)

    AI recommended 15 alternatives but never named AprilNEA/AChat. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source solutions exist for managing team AI conversations with various LLMs?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangServe
    3. LlamaIndex
    4. Open WebUI
    5. Chatbot UI
    6. LocalAI
    7. FlowiseAI
    8. PrivateGPT

    AI recommended 8 alternatives but never named AprilNEA/AChat. 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 AprilNEA/AChat?
    pass
    AI named AprilNEA/AChat explicitly

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

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

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

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AprilNEA/AChat — 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