RRepoGEO

REPOGEO REPORT · LITE

EmbeddedLLM/JamAIBase

Default branch main · commit 09cc9889 · scanned 5/13/2026, 7:12:47 AM

GitHub: 1,093 stars · 39 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 EmbeddedLLM/JamAIBase, 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
    Clarify 'embedded' terminology in README's opening paragraph

    Why:

    CURRENT
    JamAI Base is an open-source RAG (Retrieval-Augmented Generation) backend platform that integrates an embedded database (SQLite) and an embedded vector database (LanceDB) with managed memory and RAG capabilities.
    COPY-PASTE FIX
    JamAI Base is an open-source RAG (Retrieval-Augmented Generation) backend platform for building AI applications with a collaborative spreadsheet interface. It integrates a self-contained SQLite database and LanceDB vector database, providing managed memory and RAG capabilities. *Note: 'embedded' refers to the integrated database technology, not deployment on embedded hardware devices.*
  • mediumcomparison#2
    Add a comparison section to differentiate from generic tools

    Why:

    COPY-PASTE FIX
    Add a new section titled '## JamAI Base vs. Alternatives' or '## Why JamAI Base?' that highlights how JamAI Base provides a complete, integrated RAG backend platform with a spreadsheet UI, unlike individual databases (SQLite, LanceDB), web frameworks (Flask, Django), or LLM libraries (LangChain, LlamaIndex).
  • lowtopics#3
    Add 'ai-platform' and 'collaborative-ai' to topics

    Why:

    CURRENT
    agents, ai, ai-agents-framework, baas, backend-as-a-service, chatbot, chatgpt, intelligent-spreadsheet, lancedb, llama3-1, llm, llm-ops, orchestration, python, rag, retrieval-augmented-generation, serverless, spreadsheet, svelte, workflow
    COPY-PASTE FIX
    agents, ai, ai-agents-framework, ai-platform, baas, backend-as-a-service, chatbot, chatgpt, collaborative-ai, intelligent-spreadsheet, lancedb, llama3-1, llm, llm-ops, orchestration, python, rag, retrieval-augmented-generation, serverless, spreadsheet, svelte, workflow

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 EmbeddedLLM/JamAIBase
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SQLite
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SQLite · recommended 2×
  2. Google Sheets API · recommended 1×
  3. Flask · recommended 1×
  4. Django · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How to build a RAG application backend with a collaborative spreadsheet interface?
    you: not recommended
    AI recommended (in order):
    1. Google Sheets API
    2. Flask
    3. Django
    4. LangChain
    5. LlamaIndex
    6. Pinecone
    7. Weaviate
    8. Chroma
    9. Airtable
    10. Retool
    11. AppSmith
    12. Budibase
    13. Streamlit
    14. Gradio
    15. Pandas
    16. PostgreSQL
    17. SQLite
    18. Microsoft Excel Online API
    19. Next.js
    20. React
    21. AG Grid
    22. Handsontable
    23. FastAPI
    24. Socket.IO

    AI recommended 24 alternatives but never named EmbeddedLLM/JamAIBase. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source platform for LLM orchestration with embedded vector and SQL databases.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. ChromaDB (chroma-ai/chroma)
    3. SQLite
    4. LlamaIndex (run-llama/llama_index)
    5. LanceDB (lancedb/lancedb)
    6. DuckDB (duckdb/duckdb)
    7. Haystack (deepset-ai/haystack)
    8. FAISS (facebookresearch/faiss)
    9. Annoy (spotify/annoy)
    10. FlowiseAI (FlowiseAI/Flowise)
    11. Instructor (jxnl/instructor)
    12. Pydantic (pydantic/pydantic)
    13. sentence-transformers (UKPLab/sentence-transformers)

    AI recommended 13 alternatives but never named EmbeddedLLM/JamAIBase. 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 EmbeddedLLM/JamAIBase?
    pass
    AI named EmbeddedLLM/JamAIBase explicitly

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

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

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

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EmbeddedLLM/JamAIBase — 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