RRepoGEO

REPOGEO REPORT · LITE

ikatsov/tensor-house

Default branch master · commit a8ebefd4 · scanned 6/18/2026, 5:27:08 PM

GitHub: 1,446 stars · 508 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
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 ikatsov/tensor-house, 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 the README's opening paragraph to clarify its unique value as enterprise solution templates, not a platform or library

    Why:

    CURRENT
    TensorHouse is a collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more. The goal of the project is to provide a toolkit for rapid readiness assessment, exploratory data analysis, and prototyping of various modeling approaches for typical enterprise AI/ML/data science projects.
    COPY-PASTE FIX
    TensorHouse is a curated collection of **ready-to-use reference Jupyter notebooks and demo AI/ML applications**, specifically designed for **enterprise use cases** like marketing, pricing, and supply chain. Unlike general-purpose ML platforms or low-level utility libraries, TensorHouse provides concrete, industry-proven solution templates and prototypes to accelerate your project development and readiness assessment for typical enterprise AI/ML/data science projects.
  • mediumhomepage#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://github.com/ikatsov/tensor-house (or a dedicated project site if one exists)
  • mediumtopics#3
    Expand repository topics to include more specific terms related to enterprise solutions and notebook collections

    Why:

    CURRENT
    ai, customer-analysis, data-science, deep-learning, llm, machine-learning, marketing, models, personalization, reinforcement-learning, supply-chain
    COPY-PASTE FIX
    ai, customer-analysis, data-science, deep-learning, llm, machine-learning, marketing, models, personalization, reinforcement-learning, supply-chain, enterprise-ai, ai-solutions, ml-prototypes, jupyter-notebooks

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 ikatsov/tensor-house
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Microsoft Azure Machine Learning
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Microsoft Azure Machine Learning · recommended 2×
  2. Google Cloud Vertex AI · recommended 1×
  3. Amazon SageMaker · recommended 1×
  4. DataRobot · recommended 1×
  5. H2O.ai · recommended 1×
  • CATEGORY QUERY
    How to quickly prototype AI/ML solutions for enterprise marketing and supply chain problems?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Vertex AI
    2. Amazon SageMaker
    3. Microsoft Azure Machine Learning
    4. DataRobot
    5. H2O.ai
    6. RapidMiner

    AI recommended 6 alternatives but never named ikatsov/tensor-house. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a toolkit with demo AI/ML applications for enterprise data science projects.
    you: not recommended
    AI recommended (in order):
    1. H2O.ai H2O Wave
    2. H2O-3
    3. Driverless AI
    4. Databricks Lakehouse Platform
    5. Google Cloud Vertex AI Workbench
    6. Microsoft Azure Machine Learning
    7. Amazon SageMaker Studio
    8. Domino Data Lab

    AI recommended 8 alternatives but never named ikatsov/tensor-house. 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 ikatsov/tensor-house?
    pass
    AI did not name ikatsov/tensor-house — 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?

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

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

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ikatsov/tensor-house — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite