RRepoGEO

REPOGEO REPORT · LITE

Dataherald/dataherald

Default branch main · commit f8946182 · scanned 6/24/2026, 12:42:12 PM

GitHub: 3,637 stars · 264 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 the README H1 to specify the product's core function

    Why:

    CURRENT
    # Dataherald monorepo
    COPY-PASTE FIX
    # Dataherald: Open-Source Natural Language to SQL Engine
  • mediumreadme#2
    Add a 'Why Dataherald?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why Dataherald?' or '## Dataherald vs. Alternatives', that explicitly highlights its unique value as a complete, open-source, self-hostable NL2SQL platform for enterprise data, differentiating it from both commercial tools and generic frameworks.
  • lowabout#3
    Refine the 'About' description for clarity and consistency

    Why:

    CURRENT
    Interact with your SQL database, Natural Language to SQL using LLMs
    COPY-PASTE FIX
    An open-source engine for enterprise-level Natural Language to SQL, enabling interaction with your SQL database using LLMs.

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
ThoughtSpot
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ThoughtSpot · recommended 1×
  2. Tableau Ask Data · recommended 1×
  3. Power BI Q&A · recommended 1×
  4. Seek AI · recommended 1×
  5. DataChat · recommended 1×
  • CATEGORY QUERY
    How can I allow non-technical users to query my database with natural language?
    you: not recommended
    AI recommended (in order):
    1. ThoughtSpot
    2. Tableau Ask Data
    3. Power BI Q&A
    4. Seek AI
    5. DataChat
    6. Apache Superset
    7. OpenAI API

    AI recommended 7 alternatives but never named Dataherald/dataherald. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source engines generate SQL queries from natural language using large language models?
    you: not recommended
    AI recommended (in order):
    1. SQLFlow
    2. Hugging Face Transformers
    3. Rasa
    4. LangChain
    5. Haystack

    AI recommended 5 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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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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Dataherald/dataherald — RepoGEO report