RRepoGEO

REPOGEO REPORT · LITE

intel/BigDL

Default branch main · commit 7c111301 · scanned 5/24/2026, 2:26:16 PM

GitHub: 2,695 stars · 730 forks

AI VISIBILITY SCORE
68 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 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 intel/BigDL, 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
    Elevate PPML's visibility in the README overview

    Why:

    CURRENT
    PPML: Secure Big Data and AI (with SGX/TDX Hardware Security) is listed as one of many libraries.
    COPY-PASTE FIX
    Add a sentence to the main 'Overview' paragraph or immediately after it, highlighting PPML's unique offering. For example: 'BigDL seamlessly scales your data analytics & AI applications from laptop to cloud, with the following libraries: ... Additionally, BigDL offers **PPML** for secure big data and AI, leveraging SGX/TDX hardware security for confidential computing.'
  • mediumtopics#2
    Add specific topics for secure AI and confidential computing

    Why:

    CURRENT
    analytics-zoo, apache-spark, bigdl, deep-neural-network, distributed-deep-learning, keras-tensorflow, python, pytorch, scala
    COPY-PASTE FIX
    analytics-zoo, apache-spark, bigdl, deep-neural-network, distributed-deep-learning, keras-tensorflow, python, pytorch, scala, secure-ai, confidential-computing, sgx, tdx
  • lowreadme#3
    Clarify LLM deprecation in README's library list

    Why:

    CURRENT
    - `LLM` (deprecated - please use IPEX-LLM instead): Optimizaed large language model library for Intel CPU and GPU
    COPY-PASTE FIX
    Remove the `LLM` entry from the list of libraries under the 'Overview' section, relying on the `IMPORTANT` note at the top for guidance on `ipex-llm`.

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
1 / 2
50% of queries surface intel/BigDL
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
horovod/horovod
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. horovod/horovod · recommended 1×
  2. JohnSnowLabs/spark-nlp · recommended 1×
  3. yahoo/TensorFlowOnSpark · recommended 1×
  4. PyTorch on Spark · recommended 1×
  5. mlflow/mlflow · recommended 1×
  • CATEGORY QUERY
    How to scale deep learning models built with PyTorch or TensorFlow on Apache Spark?
    you: #3
    AI recommended (in order):
    1. Horovod (horovod/horovod)
    2. Spark NLP (JohnSnowLabs/spark-nlp)
    3. BigDL (intel-analytics/BigDL) ← you
    4. TensorFlowOnSpark (yahoo/TensorFlowOnSpark)
    5. PyTorch on Spark
    6. MLflow (mlflow/mlflow)
    Show full AI answer
  • CATEGORY QUERY
    Need a library for secure deep learning on large datasets using hardware-based protection.
    you: not recommended
    AI recommended (in order):
    1. Intel SGX SDK
    2. OpenVINO
    3. TensorFlow
    4. PyTorch
    5. Microsoft Azure Confidential Computing
    6. Open Enclave SDK
    7. Google Cloud Confidential Computing
    8. Graphene
    9. Occlum
    10. IBM HEE Toolkit
    11. PySyft
    12. Conclave

    AI recommended 12 alternatives but never named intel/BigDL. 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 intel/BigDL?
    pass
    AI named intel/BigDL explicitly

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

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