RRepoGEO

REPOGEO REPORT · LITE

finic-ai/doctran

Default branch main · commit 3162c54e · scanned 5/30/2026, 1:37:57 AM

GitHub: 507 stars · 41 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 finic-ai/doctran, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A modular, declarative framework for transforming messy, unstructured text into clean, labelled data using LLMs and natural language instructions.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, document-processing, data-extraction, natural-language-processing, openai, pydantic, document-transformation, unstructured-data
  • mediumreadme#3
    Strengthen README's opening value proposition

    Why:

    CURRENT
    There are certain applications that require documents to be parsed where human-level judgement matters more than speed. E.g. labelling transactions, or extracting semantic information from texts. In these cases, RegEx can be too inflexible, but LLMs are ideal. One way to think of Doctran is a LLM-powered black box where messy strings go in and nice, clean, labelled strings come out. Another way to think about it is a modular, declarative wrapper over OpenAI's functional calling feature that significantly improves the developer experience.
    COPY-PASTE FIX
    Doctran is a modular, declarative framework designed for robust document transformation using LLMs, offering a significant improvement in developer experience over direct LLM API calls for parsing complex, unstructured strings into clean, labelled data. It excels in applications where human-level judgment is crucial, such as labelling transactions or extracting semantic information, where traditional RegEx falls short and generic LLM frameworks lack specialized structure.

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 finic-ai/doctran
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. LangChain · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Anthropic Claude · recommended 1×
  5. Google Gemini · recommended 1×
  • CATEGORY QUERY
    How to transform messy, unstructured text into clean, labelled data using LLMs?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude
    3. Google Gemini
    4. Mistral AI
    5. Snorkel AI
    6. Label Studio
    7. Argilla
    8. LangChain
    9. LlamaIndex

    AI recommended 9 alternatives but never named finic-ai/doctran. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an LLM framework to parse complex documents, more flexible than regular expressions.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Deepset's Haystack
    5. OpenAI API
    6. Anthropic Claude API

    AI recommended 6 alternatives but never named finic-ai/doctran. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • 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 finic-ai/doctran?
    pass
    AI named finic-ai/doctran explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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finic-ai/doctran — 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