REPOGEO REPORT · LITE

QuivrHQ/MegaParse

Default branch main · commit ba9a24ae · scanned 5/14/2026, 7:36:56 PM

GitHub: 7,367 stars · 413 forks

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 QuivrHQ/MegaParse, 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 LLM ingestion

Why:

CURRENT

# MegaParse - Your Parser for every type of documents

MegaParse is a powerful and versatile parser that can handle various types of documents with ease. Whether you're dealing with text, PDFs, Powerpoint presentations, Word documents MegaParse has got you covered. Focus on having no information loss during parsing.

COPY-PASTE FIX

# MegaParse - The LLM-Optimized Document Parser

MegaParse is a powerful and versatile parser specifically designed for Large Language Model (LLM) ingestion, ensuring no information loss when processing diverse documents like PDFs, PowerPoints, and Word files. It provides a clean, structured output ideal for RAG applications and AI processing.

mediumtopics#2

Expand topics to include LLM-specific processing terms

Why:

CURRENT

docx, llm, parser, pdf, powerpoint

COPY-PASTE FIX

docx, llm, parser, pdf, powerpoint, llm-ingestion, rag, document-ai, ai-processing, unstructured-data, document-parser

lowreadme#3

Add a 'Comparison' section to the README

Why:

COPY-PASTE FIX

## Comparison

While tools like Unstructured.io and Apache Tika offer broad document parsing capabilities, MegaParse is uniquely optimized for Large Language Model (LLM) ingestion. Our core focus is on preserving all structural and semantic information with 'no information loss,' ensuring the highest quality data for Retrieval Augmented Generation (RAG) and other AI applications. MegaParse prioritizes efficiency and a clean, LLM-ready output, making it ideal for developers building robust AI systems.

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 QuivrHQ/MegaParse

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Unstructured.io

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Unstructured.io · recommended 2×
Apache Tika · recommended 2×
Google Cloud Document AI · recommended 2×
PDFMiner.six · recommended 2×
PyMuPDF (Fitz) · recommended 1×

CATEGORY QUERY
How can I efficiently parse PDFs, Word, and PowerPoint documents for large language model ingestion?
you: not recommended
AI recommended (in order):
1. Unstructured.io
2. Apache Tika
3. PyMuPDF (Fitz)
4. python-docx
5. python-pptx
6. Microsoft Azure AI Document Intelligence
7. Google Cloud Document AI
8. PDFMiner.six
AI recommended 8 alternatives but never named QuivrHQ/MegaParse. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a document parsing tool that retains all structural information and content for AI processing.
you: not recommended
AI recommended (in order):
1. LayoutParser
2. Apache Tika
3. Unstructured.io
4. PDFMiner.six
5. Microsoft Azure Form Recognizer / Document Intelligence
6. Google Cloud Document AI
7. Amazon Textract
AI recommended 7 alternatives but never named QuivrHQ/MegaParse. 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 QuivrHQ/MegaParse?
pass
AI named QuivrHQ/MegaParse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts QuivrHQ/MegaParse in production, what risks or prerequisites should they evaluate first?
pass
AI named QuivrHQ/MegaParse 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 QuivrHQ/MegaParse solve, and who is the primary audience?
pass
AI named QuivrHQ/MegaParse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of QuivrHQ/MegaParse. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/QuivrHQ/MegaParse.svg)](https://repogeo.com/en/r/QuivrHQ/MegaParse)

HTML

<a href="https://repogeo.com/en/r/QuivrHQ/MegaParse"><img src="https://repogeo.com/badge/QuivrHQ/MegaParse.svg" alt="RepoGEO" /></a>

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QuivrHQ/MegaParse — 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