RRepoGEO

REPOGEO REPORT · LITE

Zipstack/unstract

Default branch main · commit 7aba21f6 · scanned 6/21/2026, 9:41:20 AM

GitHub: 6,666 stars · 633 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 Zipstack/unstract, 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 opening to emphasize Unstract as a complete platform

    Why:

    CURRENT
    Unstract uses LLMs to extract structured JSON from documents — PDFs, images, scans, you name it. Define what you want to extract using natural language prompts, and deploy as an API or ETL pipeline.
    COPY-PASTE FIX
    Unstract is an **open-source platform** that leverages LLMs to extract structured JSON from unstructured documents like PDFs, images, and scans. It provides a complete solution to define extraction logic with natural language prompts and deploy it as robust APIs or integrate into ETL pipelines.
  • mediumtopics#2
    Add more specific topics to highlight Unstract's platform and application nature

    Why:

    CURRENT
    ai-agents, data-engineering, document-ai, generative-ai, idp, json-extraction, llm, mcp-server, ocr, pdf-extraction, prompt-engineering, structured-output
    COPY-PASTE FIX
    ai-agents, data-engineering, document-ai, generative-ai, idp, json-extraction, llm, mcp-server, ocr, pdf-extraction, prompt-engineering, structured-output, **data-extraction-platform, llm-applications, etl-tool, api-first**
  • lowreadme#3
    Expand the comparison section to differentiate from generic LLM libraries/models

    Why:

    CURRENT
    The README excerpt shows a table header "Current State vs. Unstract" but no content.
    COPY-PASTE FIX
    Add a new section to the README, for example, under the title 'Why Unstract? A Complete Extraction Platform,' that explicitly contrasts Unstract's end-to-end solution capabilities (API deployment, ETL integration, full pipeline management) with generic LLM libraries or models that require extensive custom development for similar outcomes.

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 Zipstack/unstract
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 · recommended 2×
  2. LlamaIndex · recommended 1×
  3. LangChain · recommended 1×
  4. OpenAI Functions · recommended 1×
  5. Anthropic Tools · recommended 1×
  • CATEGORY QUERY
    Need a tool to extract structured JSON from various unstructured documents using LLMs.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. OpenAI Functions
    4. Anthropic Tools
    5. Instructor
    6. Haystack
    7. GPT-4
    8. Claude 3
    9. Llama 3
    10. Unstructured.io

    AI recommended 10 alternatives but never named Zipstack/unstract. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good options for LLM-driven data extraction for API deployments and ETL?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5 Turbo
    2. Anthropic Claude 3
    3. Mistral Large / Mixtral 8x7B
    4. Google Gemini 1.5 Pro
    5. Llama 3
    6. Structured (unstructured-io/unstructured)
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)

    AI recommended 8 alternatives but never named Zipstack/unstract. 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 Zipstack/unstract?
    pass
    AI named Zipstack/unstract explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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