RRepoGEO

REPOGEO REPORT · LITE

feast-dev/feast

Default branch master · commit 5f1fa0d9 · scanned 5/25/2026, 9:26:42 PM

GitHub: 7,050 stars · 1,327 forks

AI VISIBILITY SCORE
89 /100
Healthy
Category recall
2 / 2
Avg rank #2.0 when recommended
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 feast-dev/feast, 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 README opening to emphasize core differentiators

    Why:

    CURRENT
    Feast (Feature Store) is an open source feature store for machine learning. Feast is the fastest path to manage existing infrastructure to productionize analytic data for model training and online inference.
    COPY-PASTE FIX
    Feast (Feature Store) is the open-source, cloud-agnostic feature store for machine learning, providing a standardized framework to manage the entire lifecycle of ML features. It offers the fastest path to productionize analytic data for model training and online inference using your existing infrastructure.
  • mediumcomparison#2
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Feast vs. Other Feature Stores', outlining key differentiators like its open-source nature, cloud-agnostic design, and focus on integrating with existing infrastructure.
  • lowreadme#3
    Streamline initial README content for immediate clarity

    Why:

    COPY-PASTE FIX
    Review the top of the README to ensure the 'Overview' section's first paragraph is immediately visible, potentially by moving badges and non-essential links to a dedicated section or below the main introduction.

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
2 / 2
100% of queries surface feast-dev/feast
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
15%
Of all named tools, what % are you?
Top rival
Tecton
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Tecton · recommended 2×
  2. Hopsworks Feature Store · recommended 1×
  3. Amazon SageMaker Feature Store · recommended 1×
  4. Databricks Feature Store · recommended 1×
  5. Verta MLOps Platform · recommended 1×
  • CATEGORY QUERY
    How can I consistently manage machine learning features for both online inference and offline training?
    you: #3
    AI recommended (in order):
    1. Hopsworks Feature Store
    2. Tecton
    3. Feast ← you
    4. Amazon SageMaker Feature Store
    5. Databricks Feature Store
    6. Verta MLOps Platform
    Show full AI answer
  • CATEGORY QUERY
    What open-source platforms help engineers manage the full lifecycle of ML features?
    you: #1
    AI recommended (in order):
    1. Feast ← you
    2. Hopsworks
    3. MLflow
    4. Tecton
    5. ZenML
    6. CML
    7. DVC
    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 feast-dev/feast?
    pass
    AI named feast-dev/feast explicitly

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

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

    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
  • Prioritized action items8 vs 3 in Lite