RRepoGEO

REPOGEO REPORT · LITE

kaito-project/aikit

Default branch main · commit f759fb5c · scanned 6/4/2026, 5:36:21 AM

GitHub: 526 stars · 57 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 kaito-project/aikit, 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 the README's opening paragraph to highlight core capabilities

    Why:

    CURRENT
    AIKit is a comprehensive platform to quickly get started to host, deploy, build and fine-tune large language models (LLMs).
    COPY-PASTE FIX
    AIKit is a Kubernetes-native platform for easily fine-tuning, packaging as OCI artifacts, and deploying open-source LLMs, enabling local development with Docker/Podman and seamless scaling to production.
  • mediumtopics#2
    Add specific topics for OCI packaging and local inference

    Why:

    CURRENT
    ai, buildkit, chatgpt, docker, fine-tuning, finetuning, gemma, gpt, inference, kubernetes, large-language-models, llama, llm, localllama, mistral, mixtral, nvidia, open-llm, open-source-llm, openai
    COPY-PASTE FIX
    ai, buildkit, chatgpt, docker, fine-tuning, finetuning, gemma, gpt, inference, kubernetes, large-language-models, llama, llm, localllama, mistral, mixtral, nvidia, open-llm, open-source-llm, openai, oci-packaging, llm-packaging, local-inference, openai-api-compatibility
  • mediumcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., '## Comparison to Alternatives', explaining how AIKit differs from or complements tools like Ollama, LM Studio, and LocalAI, especially highlighting its Kubernetes-native and OCI packaging strengths.

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 kaito-project/aikit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ollama
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ollama · recommended 2×
  2. LM Studio · recommended 1×
  3. text-generation-webui · recommended 1×
  4. Axolotl · recommended 1×
  5. Hugging Face Transformers Library · recommended 1×
  • CATEGORY QUERY
    How can I easily fine-tune and deploy open-source LLMs on my local machine?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. text-generation-webui
    4. Axolotl
    5. Hugging Face Transformers Library
    6. Accelerate
    7. Llama.cpp

    AI recommended 7 alternatives but never named kaito-project/aikit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to package LLMs as OCI artifacts and run local inference with OpenAI API?
    you: not recommended
    AI recommended (in order):
    1. LocalAI
    2. Ollama
    3. vLLM
    4. FastAPI
    5. Flask
    6. TGI (Text Generation Inference)
    7. MLflow

    AI recommended 7 alternatives but never named kaito-project/aikit. 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 kaito-project/aikit?
    pass
    AI named kaito-project/aikit explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of kaito-project/aikit. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/kaito-project/aikit.svg)](https://repogeo.com/en/r/kaito-project/aikit)
HTML
<a href="https://repogeo.com/en/r/kaito-project/aikit"><img src="https://repogeo.com/badge/kaito-project/aikit.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

kaito-project/aikit — 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