RRepoGEO

REPOGEO REPORT · LITE

instill-ai/instill-core

Default branch main · commit 74e0a597 · scanned 5/19/2026, 4:57:09 AM

GitHub: 2,310 stars · 126 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
33 /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
2 / 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 instill-ai/instill-core, 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
  • highabout#1
    Refine the 'About' description to highlight unique specialization

    Why:

    CURRENT
    🔮 Instill Core is a full-stack AI infrastructure tool for data, model and pipeline orchestration, designed to streamline every aspect of building versatile AI-first applications
    COPY-PASTE FIX
    🔮 Instill Core is a full-stack AI infrastructure platform for orchestrating data, models, and pipelines, specifically designed for building versatile AI-first applications with unstructured data processing, open-source LLM hosting, and RAG capabilities.
  • mediumtopics#2
    Add 'rag' to repository topics

    Why:

    CURRENT
    ai, api, cli, developer-tools, etl, generative-ai, golang, gpt, hacktoberfest, llm, low-code, no-code, open-source, pipeline, python, stable-diffusion, typescript, unstructured-data
    COPY-PASTE FIX
    ai, api, cli, developer-tools, etl, generative-ai, golang, gpt, hacktoberfest, llm, low-code, no-code, open-source, pipeline, python, rag, stable-diffusion, typescript, unstructured-data
  • lowreadme#3
    Clarify license details in README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [Specify License Name(s) here, e.g., Apache-2.0 and MIT] - see the [LICENSE](LICENSE) file for 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 instill-ai/instill-core
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Apache Airflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Apache Airflow · recommended 1×
  2. Kubeflow Pipelines · recommended 1×
  3. MLflow · recommended 1×
  4. Prefect · recommended 1×
  5. AWS Step Functions · recommended 1×
  • CATEGORY QUERY
    How to build and orchestrate AI pipelines for unstructured data processing efficiently?
    you: not recommended
    AI recommended (in order):
    1. Apache Airflow
    2. Kubeflow Pipelines
    3. MLflow
    4. Prefect
    5. AWS Step Functions
    6. Azure Data Factory
    7. Google Cloud Composer

    AI recommended 7 alternatives but never named instill-ai/instill-core. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source platforms simplify hosting LLMs and building RAG applications with ETL?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack (deepset/Haystack)
    4. Ollama
    5. vLLM
    6. Weaviate
    7. Airflow

    AI recommended 7 alternatives but never named instill-ai/instill-core. 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 instill-ai/instill-core?
    pass
    AI named instill-ai/instill-core explicitly

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

  • If a team adopts instill-ai/instill-core in production, what risks or prerequisites should they evaluate first?
    pass
    AI named instill-ai/instill-core 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 instill-ai/instill-core solve, and who is the primary audience?
    pass
    AI did not name instill-ai/instill-core — likely talking about a different project

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

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instill-ai/instill-core — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite