RRepoGEO

REPOGEO REPORT · LITE

apple/coreai-models

Default branch main · commit 1522e5aa · scanned 6/17/2026, 6:17:45 AM

GitHub: 1,029 stars · 77 forks

AI VISIBILITY SCORE
28 /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
2 / 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 apple/coreai-models, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumhomepage#1
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://developer.apple.com/machine-learning/core-ai/
  • lowreadme#2
    Clarify the specific model format and framework in the README's opening

    Why:

    CURRENT
    Model export recipes, Python primitives, and Swift runtime utilities for building on-device AI with Core AI.
    COPY-PASTE FIX
    Model export recipes, Python primitives, and Swift runtime utilities for building on-device AI with Apple's Core AI framework, producing `.aimodel` files for efficient 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 apple/coreai-models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Core ML Tools
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML Tools · recommended 2×
  2. TensorFlow Lite · recommended 2×
  3. Core ML · recommended 1×
  4. Create ML · recommended 1×
  5. Metal Performance Shaders · recommended 1×
  • CATEGORY QUERY
    How to integrate and run machine learning models efficiently on Apple devices using Swift?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Create ML
    3. Core ML Tools
    4. Metal Performance Shaders
    5. Vision Framework
    6. SoundAnalysis Framework
    7. TensorFlow Lite

    AI recommended 7 alternatives but never named apple/coreai-models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to prepare and optimize machine learning models for on-device inference using Python?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. ONNX Runtime
    3. PyTorch Mobile
    4. OpenVINO Toolkit
    5. Core ML Tools
    6. Apache TVM

    AI recommended 6 alternatives but never named apple/coreai-models. 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 apple/coreai-models?
    pass
    AI named apple/coreai-models explicitly

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

  • If a team adopts apple/coreai-models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named apple/coreai-models 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 apple/coreai-models solve, and who is the primary audience?
    pass
    AI did not name apple/coreai-models — likely talking about a different project

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

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  • Brand-free category queries5 vs 2 in Lite
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