RRepoGEO

REPOGEO REPORT · LITE

omnimind-ai/OmniInfer

Default branch main · commit 79c30573 · scanned 6/1/2026, 9:51:48 PM

GitHub: 806 stars · 5 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 omnimind-ai/OmniInfer, 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
  • hightopics#1
    Add relevant topics for categorization

    Why:

    COPY-PASTE FIX
    llm-inference, edge-ai, vlm-inference, local-llm, inference-engine, ai-inference, on-device-ai, mlops-tools, unified-inference
  • highreadme#2
    Add an introductory paragraph clarifying broader scope

    Why:

    CURRENT
    The current README structure places navigation links and the "Demo" section immediately after the tagline "Easy, fast, and private LLM & VLM inference for every device".
    COPY-PASTE FIX
    Insert this sentence after the tagline and before the navigation links: "OmniInfer provides a unified, efficient, and easy-to-use inference infrastructure designed for deploying a wide range of AI models, including LLMs and VLMs, directly on edge devices."
  • mediumhomepage#3
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    Add the URL to your project's main website or comprehensive documentation portal (e.g., "https://omnimind.ai/omniinfer").

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 omnimind-ai/OmniInfer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
llama.cpp
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. llama.cpp · recommended 1×
  2. Ollama · recommended 1×
  3. LM Studio · recommended 1×
  4. Hugging Face transformers · recommended 1×
  5. bitsandbytes · recommended 1×
  • CATEGORY QUERY
    How can I run large language models efficiently on local devices?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. Ollama
    3. LM Studio
    4. Hugging Face transformers
    5. bitsandbytes
    6. AutoGPTQ
    7. MLC LLM
    8. Text Generation WebUI (oobabooga/text-generation-webui)

    AI recommended 8 alternatives but never named omnimind-ai/OmniInfer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide unified inference for AI models on various edge devices?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO
    2. ONNX Runtime
    3. TensorFlow Lite
    4. NVIDIA TensorRT
    5. Edge Impulse
    6. Apache TVM

    AI recommended 6 alternatives but never named omnimind-ai/OmniInfer. 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 omnimind-ai/OmniInfer?
    pass
    AI named omnimind-ai/OmniInfer explicitly

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

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

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

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MARKDOWN (README)
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omnimind-ai/OmniInfer — 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