REPOGEO REPORT · LITE

business-science/ai-data-science-team

Default branch master · commit 4ffeb7f3 · scanned 5/25/2026, 1:26:44 AM

GitHub: 5,227 stars · 911 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 business-science/ai-data-science-team, 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 H1 to clarify project category

Why:

CURRENT

AI Data Science Team is a Python library of specialized agents for common data science workflows, plus a flagship app: **AI Pipeline Studio**.

COPY-PASTE FIX

AI Data Science Team is an open-source Python library that orchestrates specialized AI agents for common data science workflows, featuring the **AI Pipeline Studio** – a local, visual, and reproducible environment for building data science pipelines.

highhomepage#2
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://github.com/business-science/ai-data-science-team
```

mediumcomparison#3

Add a 'Comparison' section to the README

Why:

COPY-PASTE FIX

Add a new section titled 'Comparison' or 'Why AI Data Science Team?' with text like: 'Unlike enterprise AutoML platforms (e.g., DataRobot, H2O Driverless AI) or cloud MLOps services (e.g., Azure ML, Vertex AI), AI Data Science Team is an open-source, local-first Python framework. It empowers data scientists with an agent-orchestration layer and a visual Streamlit-based studio for building reproducible pipelines, offering greater control and flexibility without vendor lock-in.'

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 business-science/ai-data-science-team

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

DataRobot

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

DataRobot · recommended 1×
H2O Driverless AI · recommended 1×
Google Cloud AutoML · recommended 1×
Azure Machine Learning · recommended 1×
Amazon SageMaker Autopilot · recommended 1×

CATEGORY QUERY
How can I automate common data science tasks using intelligent agents?
you: not recommended
AI recommended (in order):
1. DataRobot
2. H2O Driverless AI
3. Google Cloud AutoML
4. Azure Machine Learning
5. Amazon SageMaker Autopilot
6. TPOT
7. AutoGluon
AI recommended 7 alternatives but never named business-science/ai-data-science-team. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools offer visual, reproducible pipelines for accelerating data science workflows?
you: not recommended
AI recommended (in order):
1. Dataiku DSS (Data Science Studio)
2. KNIME Analytics Platform
3. Azure Machine Learning Studio (classic) / Azure Machine Learning Designer
4. Google Cloud Vertex AI Pipelines
5. Domino Data Lab
6. Apache Airflow (with UI)
7. Prefect
AI recommended 7 alternatives but never named business-science/ai-data-science-team. 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 business-science/ai-data-science-team?
pass
AI did not name business-science/ai-data-science-team — 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 business-science/ai-data-science-team in production, what risks or prerequisites should they evaluate first?
pass
AI named business-science/ai-data-science-team 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 business-science/ai-data-science-team solve, and who is the primary audience?
pass
AI named business-science/ai-data-science-team explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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Brand-free category queries5 vs 2 in Lite
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