RRepoGEO

REPOGEO REPORT · LITE

Kiln-AI/Kiln

Default branch main · commit 782ef77a · scanned 6/30/2026, 2:12:00 AM

GitHub: 4,941 stars · 375 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)

2 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 Kiln-AI/Kiln, 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 main heading to specify core capabilities

    Why:

    CURRENT
    <h3 align="center">
  A free app and open-source library to build better AI products.
</h3>
    COPY-PASTE FIX
    <h3 align="center">
  The comprehensive open-source platform and app for building, evaluating, and optimizing AI systems, including RAG, agents, fine-tuning, and synthetic data generation.
</h3>
  • highabout#2
    Enhance the 'about' description with more explicit keywords

    Why:

    CURRENT
    Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.
    COPY-PASTE FIX
    A comprehensive open-source platform and app to build, evaluate, and optimize advanced AI systems and applications. Features include robust tools for RAG, AI agents, model fine-tuning, synthetic data generation, dataset management, and comprehensive evaluations.
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    Add a section like: 

## License 
Kiln is released under [Specify License Name(s) here, e.g., a custom license]. Please refer to the `LICENSE` file for complete details.

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 Kiln-AI/Kiln
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. Hugging Face Datasets · recommended 1×
  5. MLflow · recommended 1×
  • CATEGORY QUERY
    What tools help build, evaluate, and optimize AI models and RAG applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. Hugging Face Datasets
    5. MLflow
    6. Weights & Biases
    7. DeepEval
    8. Pinecone
    9. Weaviate
    10. Qdrant

    AI recommended 10 alternatives but never named Kiln-AI/Kiln. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I generate synthetic data and fine-tune models for AI agent development?
    you: not recommended
    AI recommended (in order):
    1. Tonic.ai
    2. Synthesized
    3. Gretel.ai
    4. OpenAI API
    5. Faker (joke2k/faker)
    6. Hugging Face Transformers (huggingface/transformers)
    7. Accelerate (huggingface/accelerate)
    8. PyTorch Lightning (Lightning-AI/lightning)
    9. Keras (keras-team/keras)
    10. OpenAI Fine-tuning API
    11. LoRA

    AI recommended 11 alternatives but never named Kiln-AI/Kiln. 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 Kiln-AI/Kiln?
    pass
    AI named Kiln-AI/Kiln explicitly

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

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

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

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Kiln-AI/Kiln — 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