RRepoGEO

REPOGEO REPORT · LITE

Dataherald/dataherald

Default branch main · commit f8946182 · scanned 5/14/2026, 1:32:13 AM

GitHub: 3,633 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 Dataherald/dataherald, 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 README H1 to emphasize product and solution

    Why:

    CURRENT
    # Dataherald monorepo
    COPY-PASTE FIX
    # Dataherald: Open-Source Natural Language to SQL Engine & API
  • mediumreadme#2
    Integrate 'API' more prominently into the README's initial value proposition

    Why:

    CURRENT
    Dataherald is a natural language-to-SQL engine built for enterprise-level question answering over relational data. It allows you to set up an API from your database that can answer questions in plain English.
    COPY-PASTE FIX
    Dataherald is an open-source natural language-to-SQL engine that allows you to easily build an API for enterprise-level question answering over your relational data. It enables you to query your relational data in plain English.
  • mediumreadme#3
    Add a 'Why Dataherald?' section highlighting differentiators

    Why:

    COPY-PASTE FIX
    ## Why Dataherald?

Dataherald stands out as an open-source, self-hostable platform for natural language to SQL. Unlike many alternatives, it emphasizes human-in-the-loop validation and feedback mechanisms to continuously improve accuracy and reliability for enterprise use cases. It provides a complete engine and API solution, not just a framework.

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 Dataherald/dataherald
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DB-GPT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DB-GPT · recommended 2×
  2. LangChain · recommended 1×
  3. OpenAI GPT-4 · recommended 1×
  4. Claude 3 · recommended 1×
  5. Google's Gemini Pro · recommended 1×
  • CATEGORY QUERY
    How can I build an API to answer natural language questions over my SQL database?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI GPT-4
    3. Claude 3
    4. Google's Gemini Pro
    5. LlamaIndex
    6. SQLFlow
    7. Hugging Face Transformers
    8. T5
    9. BART
    10. Spider
    11. WikiSQL
    12. nlq-sql
    13. DB-GPT

    AI recommended 13 alternatives but never named Dataherald/dataherald. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What LLM-powered tools convert natural language queries into SQL for data analysis?
    you: not recommended
    AI recommended (in order):
    1. ThoughtSpot
    2. DataChat
    3. Seek AI
    4. Aptus AI
    5. Vanna AI
    6. DB-GPT

    AI recommended 6 alternatives but never named Dataherald/dataherald. 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 Dataherald/dataherald?
    pass
    AI named Dataherald/dataherald explicitly

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

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

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

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Dataherald/dataherald — 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