RRepoGEO

REPOGEO REPORT · LITE

nomic-ai/nomic

Default branch main · commit b963f429 · scanned 5/16/2026, 5:12:14 PM

GitHub: 1,878 stars · 197 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)

3 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 nomic-ai/nomic, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., Apache-2.0, MIT) to clarify usage rights.
  • highabout#2
    Update the repository's 'About' description to emphasize the platform

    Why:

    CURRENT
    Nomic Developer API SDK
    COPY-PASTE FIX
    Python client for Nomic Atlas, the interactive platform for exploring, visualizing, and curating large-scale unstructured data embeddings.
  • mediumtopics#3
    Add more specific topics related to interactive data exploration and platforms

    Why:

    CURRENT
    clustering, duplicate-detection, embeddings, python, text, topic-modeling, unstructured-data
    COPY-PASTE FIX
    clustering, duplicate-detection, embeddings, python, text, topic-modeling, unstructured-data, data-visualization, interactive-data, data-exploration, ai-platform

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 nomic-ai/nomic
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
lmcinnes/umap
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. lmcinnes/umap · recommended 1×
  2. tensorflow/tensorboard · recommended 1×
  3. plotly/dash · recommended 1×
  4. OpenAI's Embedding Projector · recommended 1×
  5. streamlit/streamlit · recommended 1×
  • CATEGORY QUERY
    What are good tools for visualizing and exploring millions of unstructured data embeddings?
    you: not recommended
    AI recommended (in order):
    1. UMAP (lmcinnes/umap)
    2. TensorBoard (tensorflow/tensorboard)
    3. Plotly Dash (plotly/dash)
    4. OpenAI's Embedding Projector
    5. Streamlit (streamlit/streamlit)
    6. D3.js (d3/d3)
    7. Faiss (facebookresearch/faiss)
    8. Datashader (holoviz/datashader)
    9. HoloViews (holoviz/holoviews)
    10. Bokeh (bokeh/bokeh)

    AI recommended 10 alternatives but never named nomic-ai/nomic. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to perform scalable clustering and topic modeling on large text datasets using Python?
    you: not recommended
    AI recommended (in order):
    1. Gensim
    2. scikit-learn
    3. Dask
    4. Apache Spark
    5. Spark MLlib
    6. BERTopic
    7. HDBSCAN
    8. UMAP
    9. PaCMAP
    10. Faiss

    AI recommended 10 alternatives but never named nomic-ai/nomic. 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 nomic-ai/nomic?
    pass
    AI named nomic-ai/nomic explicitly

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

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