RRepoGEO

REPOGEO REPORT · LITE

getomnico/omni

Default branch master · commit 107823a1 · scanned 6/11/2026, 7:07:07 PM

GitHub: 729 stars · 38 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 getomnico/omni, 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 to the repository

    Why:

    COPY-PASTE FIX
    workplace-ai, ai-assistant, enterprise-search, self-hosted, llm-orchestration, knowledge-management, ai-agent, rag, postgres, rust, python, sveltekit, internal-search, secure-ai
  • highreadme#2
    Refine README's opening statement for clearer platform positioning

    Why:

    CURRENT
    <div align="center">

**Omni is an AI Assistant and Search platform for the Workplace.**

Connects to your workplace apps, helps employees find information and get work done.

[Features](#features) • [Architecture](#architecture) • Docs • [Deploy](#deployment) • [Contributing](#contributing)

</div>
    COPY-PASTE FIX
    <div align="center">

**Omni is a complete, self-hosted AI Assistant and Enterprise Search platform for the Workplace.**

It connects to your existing workplace apps, enabling employees to find information, get work done, and leverage AI agents securely within your infrastructure.

[Features](#features) • [Architecture](#architecture) • Docs • [Deploy](#deployment) • [Contributing](#contributing)

</div>
  • mediumreadme#3
    Add a 'Why Omni?' section to differentiate from frameworks

    Why:

    COPY-PASTE FIX
    ## Why Omni?

Unlike LLM frameworks (e.g., LangChain, LlamaIndex) or vector databases (e.g., Weaviate) that provide components for building AI applications, Omni is a complete, self-hosted platform. It offers an integrated AI assistant, unified enterprise search, and secure data access out-of-the-box, designed for immediate deployment in your workplace. Omni focuses on delivering a production-ready solution rather than just a toolkit.

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 getomnico/omni
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. weaviate/weaviate · recommended 2×
  3. meta-llama/llama-models · recommended 1×
  4. mistralai/mistral-src · recommended 1×
  5. tiiuae/falcon-40b · recommended 1×
  • CATEGORY QUERY
    How can I build a self-hosted AI assistant for secure workplace information retrieval?
    you: not recommended
    AI recommended (in order):
    1. Llama 2 (meta-llama/llama-models)
    2. Mistral 7B / Mixtral 8x7B (mistralai/mistral-src)
    3. Falcon (tiiuae/falcon-40b)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. Weaviate (weaviate/weaviate)
    7. Pinecone
    8. Qdrant (qdrant/qdrant)
    9. Chroma (chroma-core/chroma)
    10. Unstructured.io (Unstructured-IO/unstructured)
    11. PyPDF2 (pypdf/pypdf)
    12. pdfminer.six (pdfminer/pdfminer.six)
    13. Pandoc (jgm/pandoc)
    14. Docker (moby/moby)
    15. Kubernetes (kubernetes/kubernetes)
    16. NVIDIA Triton Inference Server (triton-inference-server/server)
    17. OpenShift
    18. Hugging Face Transformers (huggingface/transformers)

    AI recommended 18 alternatives but never named getomnico/omni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source platforms enable an LLM to search and analyze data across enterprise applications?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (llamaindex/llamaindex)
    2. LangChain (langchain-ai/langchain)
    3. Haystack (deepset-ai/haystack)
    4. OpenSearch (opensearch-project/OpenSearch)
    5. Weaviate (weaviate/weaviate)
    6. Apache Flink (apache/flink)
    7. Apache Kafka (apache/kafka)

    AI recommended 7 alternatives but never named getomnico/omni. 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 getomnico/omni?
    pass
    AI named getomnico/omni explicitly

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

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

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

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getomnico/omni — 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