REPOGEO REPORT · LITE

nlmatics/nlm-ingestor

Default branch main · commit 5f0c1b92 · scanned 5/19/2026, 8:11:54 AM

GitHub: 1,282 stars · 195 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

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 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 nlmatics/nlm-ingestor, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

rag, llm, document-parsing, pdf-parser, html-parser, ocr, document-ingestion, text-extraction, structured-data

highreadme#2

Strengthen the README's opening statement for RAG

Why:

CURRENT

# About

This repo provides the service code for llmsherpa API to connect. 
This repo contains custom RAG (retrieval augmented generation) friendly parsers for the following file formats:

COPY-PASTE FIX

# nlmatics/nlm-ingestor: RAG-Optimized Document Ingestion Service

This repository provides the server-side code for the llmsherpa API, offering custom, RAG (Retrieval Augmented Generation)-friendly parsers designed to transform diverse document types into high-quality, structured data for LLM applications. It includes advanced parsers for:

mediumabout#3

Refine the repository description for clarity and RAG focus

Why:

CURRENT

This repo provides the server side code for llmsherpa API to connect. It includes parsers for various file formats.

COPY-PASTE FIX

RAG-optimized document ingestion service providing custom, layout-aware parsers for PDF, HTML, and other formats, designed to prepare high-quality structured data for LLM applications.

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 nlmatics/nlm-ingestor

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Amazon Textract

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Amazon Textract · recommended 2×
Unstructured-IO/unstructured · recommended 1×
Layout-Parser/layout-parser · recommended 1×
tesseract-ocr/tesseract · recommended 1×
Google Cloud Vision AI · recommended 1×

CATEGORY QUERY
How to extract structured text and tables from PDFs for retrieval augmented generation?
you: not recommended
AI recommended (in order):
1. Unstructured.io (Unstructured-IO/unstructured)
2. LayoutParser (Layout-Parser/layout-parser)
3. Tesseract (tesseract-ocr/tesseract)
4. Google Cloud Vision AI
5. Nougat (facebookresearch/nougat)
6. PyMuPDF (pymupdf/PyMuPDF)
7. PDFplumber (jsvine/pdfplumber)
8. Apache Tika (apache/tika)
9. Amazon Textract
AI recommended 9 alternatives but never named nlmatics/nlm-ingestor. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Need a robust document parsing service to process complex PDFs with OCR capabilities.
you: not recommended
AI recommended (in order):
1. Amazon Textract
2. Google Cloud Document AI
3. Microsoft Azure Form Recognizer
4. Abbyy FineReader Engine SDK
5. Rossum
6. Kofax OmniPage Capture SDK
AI recommended 6 alternatives but never named nlmatics/nlm-ingestor. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
warn
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 nlmatics/nlm-ingestor?
pass
AI named nlmatics/nlm-ingestor explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts nlmatics/nlm-ingestor in production, what risks or prerequisites should they evaluate first?
pass
AI named nlmatics/nlm-ingestor 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 nlmatics/nlm-ingestor solve, and who is the primary audience?
pass
AI named nlmatics/nlm-ingestor 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 nlmatics/nlm-ingestor. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/nlmatics/nlm-ingestor.svg)](https://repogeo.com/en/r/nlmatics/nlm-ingestor)

HTML

<a href="https://repogeo.com/en/r/nlmatics/nlm-ingestor"><img src="https://repogeo.com/badge/nlmatics/nlm-ingestor.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite