RRepoGEO

REPOGEO REPORT · LITE

ArcadeData/arcadedb

Default branch main · commit d5e66666 · scanned 6/2/2026, 4:36:09 PM

GitHub: 913 stars · 113 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 ArcadeData/arcadedb, 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
    Populate the empty README H1 with a clear, keyword-rich title

    Why:

    CURRENT
    #
    COPY-PASTE FIX
    # ArcadeDB: Multi-Model DBMS for Extreme Performance (Graph, Document, Key-Value, Vector Search)
  • mediumreadme#2
    Emphasize integrated vector search capabilities early in the README

    Why:

    COPY-PASTE FIX
    Add a sentence or short paragraph near the top of the README, for example: 'ArcadeDB natively supports Vector Embeddings and advanced similarity search, making it ideal for AI-driven applications.'
  • lowreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled `## Comparison with other Multi-Model Databases` and briefly outline how ArcadeDB differentiates itself from key competitors like ArangoDB or OrientDB.

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 ArcadeData/arcadedb
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ArangoDB
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ArangoDB · recommended 1×
  2. OrientDB · recommended 1×
  3. Azure Cosmos DB · recommended 1×
  4. DataStax Enterprise (DSE) · recommended 1×
  5. MarkLogic · recommended 1×
  • CATEGORY QUERY
    What database supports multiple data models like graph, document, and key-value for extreme performance?
    you: not recommended
    AI recommended (in order):
    1. ArangoDB
    2. OrientDB
    3. Azure Cosmos DB
    4. DataStax Enterprise (DSE)
    5. MarkLogic

    AI recommended 5 alternatives but never named ArcadeData/arcadedb. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a distributed multi-model database with integrated vector search capabilities for similarity search.
    you: not recommended
    AI recommended (in order):
    1. Astra DB
    2. MongoDB Atlas
    3. Redis Enterprise
    4. Elasticsearch
    5. Milvus

    AI recommended 5 alternatives but never named ArcadeData/arcadedb. 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 ArcadeData/arcadedb?
    pass
    AI named ArcadeData/arcadedb explicitly

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

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

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

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ArcadeData/arcadedb — 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