RRepoGEO

REPOGEO REPORT · LITE

kitops-ml/kitops

Default branch main · commit d029d9fa · scanned 6/28/2026, 8:01:30 PM

GitHub: 1,380 stars · 176 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 kitops-ml/kitops, 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 OCI-specific keywords to topics

    Why:

    CURRENT
    ai, code, datasets, devops, devops-tools, gguf, hacktoberfest, kubernetes, kubernetes-deployment, ml, mlops, mlops-tools, model-interpretability, model-serving, models, opensource, platform-engineering, pytorch, sklearn, tensorflow
    COPY-PASTE FIX
    ai, code, datasets, devops, devops-tools, gguf, hacktoberfest, kubernetes, kubernetes-deployment, ml, mlops, mlops-tools, model-interpretability, model-serving, models, opensource, platform-engineering, pytorch, sklearn, tensorflow, oci-artifacts, oci-images, container-registry, ml-packaging, model-versioning
  • highreadme#2
    Clarify KitOps' scope relative to model serving tools in README

    Why:

    CURRENT
    As part of the Kubernetes AI/ML technology stack, KitOps is the preferred solution for packaging, versioning, and managing AI assets in security-conscious enterprises, governments, and cloud operators who need to self-host AI models and agents.
    COPY-PASTE FIX
    As part of the Kubernetes AI/ML technology stack, KitOps is the preferred solution for packaging, versioning, and managing AI assets in security-conscious enterprises, governments, and cloud operators who need to self-host AI models and agents. While tools like KServe and Seldon Core focus on model serving, KitOps provides the foundational OCI-compliant packaging and versioning that makes models ready for secure and standardized deployment across any environment.
  • mediumreadme#3
    Explicitly emphasize "standardization" in the README's "What is KitOps?" section

    Why:

    CURRENT
    Built on the same OCI (Open Container Initiative) technology that underlies containers, KitOps packages everything your model needs for development or production into a versioned and layered artifact stored in your existing container registry. It integrates with all your AI/ML, CI/CD, and DevOps tools.
    COPY-PASTE FIX
    Built on the same OCI (Open Container Initiative) technology that underlies containers, KitOps packages everything your model needs for development or production into a versioned and layered artifact stored in your existing container registry. By leveraging OCI, KitOps establishes a vendor-neutral standard for packaging and managing AI/ML assets, ensuring consistency and interoperability across diverse MLOps ecosystems. It integrates with all your AI/ML, CI/CD, and DevOps tools.

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 kitops-ml/kitops
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
mlflow/mlflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. mlflow/mlflow · recommended 2×
  2. iterative/dvc · recommended 1×
  3. Docker · recommended 1×
  4. git-lfs/git-lfs · recommended 1×
  5. kubernetes/kubernetes · recommended 1×
  • CATEGORY QUERY
    How to package and version AI models, datasets, and code for secure deployment?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. DVC (iterative/dvc)
    3. Docker
    4. Git LFS (git-lfs/git-lfs)
    5. Kubernetes (kubernetes/kubernetes)
    6. Pachyderm (pachyderm/pachyderm)
    7. Hugging Face Hub

    AI recommended 7 alternatives but never named kitops-ml/kitops. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for standardizing ML model packaging using OCI artifacts for Kubernetes deployment?
    you: not recommended
    AI recommended (in order):
    1. Seldon Core (SeldonIO/seldon-core)
    2. KServe (kserve/kserve)
    3. MLflow (mlflow/mlflow)
    4. Open Model Management (OMM) (open-model-management/omm)
    5. Hugging Face Optimum (huggingface/optimum)

    AI recommended 5 alternatives but never named kitops-ml/kitops. 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 kitops-ml/kitops?
    pass
    AI named kitops-ml/kitops explicitly

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

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

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

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kitops-ml/kitops — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite