RRepoGEO

REPOGEO REPORT · LITE

IBM/AssetOpsBench

Default branch main · commit 02670da9 · scanned 5/23/2026, 9:58:19 PM

GitHub: 1,598 stars · 241 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
35 /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
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 IBM/AssetOpsBench, 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 comprehensive topics to improve categorization

    Why:

    COPY-PASTE FIX
    industry-4.0, ai-agents, industrial-ai, asset-management, predictive-maintenance, benchmark, evaluation-framework, time-series-data, machine-learning, operations-maintenance
  • highabout#2
    Refine the 'About' description for clarity and specificity

    Why:

    CURRENT
    AssetOpsBench - Industry 4.0
    COPY-PASTE FIX
    A unified, open framework and benchmark for building, orchestrating, and evaluating domain-specific AI agents in Industry 4.0 asset operations and maintenance.
  • mediumreadme#3
    Emphasize 'benchmark' and target audience in the README's opening statement

    Why:

    CURRENT
    *A unified, open framework for building, orchestrating, and evaluating domain-specific AI agents in Industry 4.0.*
    COPY-PASTE FIX
    *AssetOpsBench is a unified, open framework and benchmark for building, orchestrating, and evaluating domain-specific AI agents in Industry 4.0 asset operations and maintenance. It is designed for researchers, data scientists, and developers.*

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 IBM/AssetOpsBench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OSIsoft PI System
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OSIsoft PI System · recommended 1×
  2. apache/kafka · recommended 1×
  3. Databricks Lakehouse Platform · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. keras-team/keras · recommended 1×
  • CATEGORY QUERY
    How can I build and evaluate AI agents for industrial asset management?
    you: not recommended
    AI recommended (in order):
    1. OSIsoft PI System
    2. Apache Kafka (apache/kafka)
    3. Databricks Lakehouse Platform
    4. TensorFlow (tensorflow/tensorflow)
    5. Keras (keras-team/keras)
    6. PyTorch (pytorch/pytorch)
    7. Scikit-learn (scikit-learn/scikit-learn)
    8. AWS SageMaker
    9. MLflow (mlflow/mlflow)
    10. Grafana (grafana/grafana)
    11. Prometheus (prometheus/prometheus)

    AI recommended 11 alternatives but never named IBM/AssetOpsBench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open frameworks exist for developing AI solutions in Industry 4.0 operations?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. TensorFlow Lite
    3. PyTorch Mobile
    4. Eclipse IoT
    5. Apache Flink
    6. ROS
    7. ONNX Runtime

    AI recommended 7 alternatives but never named IBM/AssetOpsBench. 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 IBM/AssetOpsBench?
    pass
    AI named IBM/AssetOpsBench explicitly

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

  • If a team adopts IBM/AssetOpsBench in production, what risks or prerequisites should they evaluate first?
    pass
    AI named IBM/AssetOpsBench 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 IBM/AssetOpsBench solve, and who is the primary audience?
    pass
    AI named IBM/AssetOpsBench 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 IBM/AssetOpsBench. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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IBM/AssetOpsBench — 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