RRepoGEO

REPOGEO REPORT · LITE

georgia-tech-db/evadb

Default branch staging · commit e5a91909 · scanned 5/24/2026, 1:27:14 PM

GitHub: 2,678 stars · 263 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 georgia-tech-db/evadb, 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 highlight unique value

    Why:

    CURRENT
    EvaDB enables software developers to build AI apps in a few lines of code. Its powerful SQL API simplifies AI app development for both structured and unstructured data.
    COPY-PASTE FIX
    EvaDB is an **AI-native database system** that allows you to **integrate AI/ML models directly into SQL queries** for processing and analyzing unstructured data like video, images, and text. It simplifies building AI-powered applications by enabling advanced analytics, such as object detection and video analytics, *within* the database.
  • mediumtopics#2
    Add more specific topics related to AI-native databases and in-database analytics

    Why:

    CURRENT
    agent, ai, auto-gpt, chatgpt, data-analysis, database, eva, gpt-4, gpt4all, hacktoberfest, huggingface, labeling, langchain, llm, object-detection, serving, video-analytics
    COPY-PASTE FIX
    agent, ai, auto-gpt, chatgpt, data-analysis, database, eva, gpt-4, gpt4all, hacktoberfest, huggingface, labeling, langchain, llm, object-detection, serving, video-analytics, ai-database, in-database-ai, sql-for-ai, unstructured-data-analytics, video-database
  • lowreadme#3
    Add a "Comparison with Alternatives" section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

EvaDB differs from traditional vector databases (like Pinecone, Weaviate, Milvus) and general-purpose databases (like PostgreSQL, MongoDB, Elasticsearch) by integrating AI/ML model inference and operations directly into a SQL-like query language. While vector databases excel at semantic search, EvaDB focuses on enabling complex AI analytics on unstructured data *within* the database, allowing for seamless integration of AI models into your data processing workflows. EvaDB can also complement vector databases by providing the AI processing layer for feature extraction and analysis before vectorization.

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 georgia-tech-db/evadb
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 1×
  2. Weaviate · recommended 1×
  3. Milvus · recommended 1×
  4. Elasticsearch · recommended 1×
  5. MongoDB · recommended 1×
  • CATEGORY QUERY
    What database system is best for building AI-powered applications that process unstructured data?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Milvus
    4. Elasticsearch
    5. MongoDB
    6. Neo4j

    AI recommended 6 alternatives but never named georgia-tech-db/evadb. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I perform AI-driven video analytics and object detection directly within a database?
    you: not recommended
    AI recommended (in order):
    1. PostgreSQL
    2. PostGIS
    3. PL/Python
    4. PL/R
    5. OpenCV (opencv/opencv)
    6. TensorFlow (tensorflow/tensorflow)
    7. PyTorch (pytorch/pytorch)
    8. YOLO
    9. YOLOv8 (ultralytics/ultralytics)
    10. SSD MobileNet
    11. SQL Server
    12. SQL Server Machine Learning Services
    13. Oracle Database
    14. Oracle Machine Learning (OML)
    15. OML4Py
    16. ClickHouse (ClickHouse/ClickHouse)
    17. ONNX (onnx/onnx)
    18. DuckDB (duckdb/duckdb)
    19. Milvus (milvus-io/milvus)
    20. Mask R-CNN

    AI recommended 20 alternatives but never named georgia-tech-db/evadb. 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 georgia-tech-db/evadb?
    pass
    AI named georgia-tech-db/evadb explicitly

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

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