RRepoGEO

REPOGEO REPORT · LITE

amaiya/onprem

Default branch master · commit 40a166b4 · scanned 5/30/2026, 12:12:15 PM

GitHub: 840 stars · 55 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 amaiya/onprem, 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
    llm, on-premise, private-llm, offline-llm, document-intelligence, rag, python, local-llm, data-privacy, generative-ai, ai-toolkit, enterprise-ai
  • highreadme#2
    Refine README tagline to emphasize secure document intelligence with local LLMs

    Why:

    CURRENT
    > A privacy-conscious toolkit for document intelligence — local by default, cloud-capable
    COPY-PASTE FIX
    > The privacy-conscious toolkit for secure document intelligence with local-first LLMs, cloud-capable.
  • mediumcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

OnPrem.LLM differentiates itself from general local LLM runners like Ollama, LM Studio, and PrivateGPT by offering a comprehensive toolkit for building advanced document intelligence pipelines with a strong focus on privacy and enterprise integration.

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 amaiya/onprem
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LM Studio
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LM Studio · recommended 2×
  2. Ollama · recommended 1×
  3. PrivateGPT · recommended 1×
  4. LocalAI · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How to run large language models on private, offline documents securely?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. PrivateGPT
    4. LocalAI
    5. Hugging Face Transformers
    6. ChromaDB
    7. FAISS
    8. NVIDIA NeMo Guardrails

    AI recommended 8 alternatives but never named amaiya/onprem. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable local LLM processing for document intelligence tasks like summarization and Q&A?
    you: not recommended
    AI recommended (in order):
    1. Ollama (ollama/ollama)
    2. LM Studio
    3. LocalAI (go-skynet/LocalAI)
    4. Llama.cpp (ggerganov/llama.cpp)
    5. Hugging Face Transformers (huggingface/transformers)
    6. PrivateGPT (imartinez/privateGPT)
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)

    AI recommended 8 alternatives but never named amaiya/onprem. 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 amaiya/onprem?
    pass
    AI named amaiya/onprem explicitly

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

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

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

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amaiya/onprem — 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