RRepoGEO

REPOGEO REPORT · LITE

vespa-engine/vespa

Default branch master · commit 046939a2 · scanned 5/27/2026, 6:26:39 PM

GitHub: 6,926 stars · 715 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
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 vespa-engine/vespa, 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 statement to emphasize 'platform' and 'real-time AI applications' and list key use cases.

    Why:

    CURRENT
    Search, make inferences in and organize vectors, tensors, text and structured data, at serving time and any scale.
    COPY-PASTE FIX
    Vespa is an open-source platform for building large-scale, real-time AI applications that combine vector search, machine learning inference, and structured data serving at any scale. It is ideal for use cases such as personalized recommendations, Retrieval Augmented Generation (RAG), and other real-time AI-powered experiences.
  • mediumabout#2
    Expand the repository description to be more specific.

    Why:

    CURRENT
    AI + Data, online. https://vespa.ai
    COPY-PASTE FIX
    Vespa is an open-source platform for real-time AI applications, combining vector search, machine learning inference, and data serving for personalized recommendations and RAG.
  • lowreadme#3
    Add a sentence to the README highlighting Vespa's core differentiator.

    Why:

    COPY-PASTE FIX
    Vespa's unified architecture combines lexical search, vector search, and deep, customizable machine learning model inference directly within the query and ranking pipeline for real-time serving of large datasets.

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 vespa-engine/vespa
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. Weaviate · recommended 1×
  3. Qdrant · recommended 1×
  4. Milvus · recommended 1×
  5. Elasticsearch · recommended 1×
  • CATEGORY QUERY
    What platform offers real-time vector search and machine learning inference for large datasets?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Qdrant
    4. Milvus
    5. Elasticsearch
    6. Kubernetes
    7. TensorFlow Serving
    8. PyTorch Serve
    9. Faiss
    10. Flask
    11. FastAPI
    12. ONNX Runtime
    13. TensorFlow Lite
    14. Redis
    15. Apache Cassandra

    AI recommended 15 alternatives but never named vespa-engine/vespa. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a scalable search engine to power personalized recommendations and RAG applications.
    you: not recommended
    AI recommended (in order):
    1. Elasticsearch (elastic/elasticsearch)
    2. OpenSearch (opensearch-project/OpenSearch)
    3. Pinecone
    4. Weaviate (weaviate/weaviate)
    5. Solr
    6. Milvus (milvus-io/milvus)

    AI recommended 6 alternatives but never named vespa-engine/vespa. 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 vespa-engine/vespa?
    pass
    AI named vespa-engine/vespa explicitly

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

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