Use Case

Stop feeding garbage into your vector database

Convert PDFs to clean Markdown for retrieval-augmented generation. Structured, chunk-ready content with tables, headings, and document hierarchy intact.

Zero data retention -- your documents are deleted immediately after parsing.

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See the difference

The quality of your RAG output is capped by the quality of your parsing. Here is what raw extraction looks like vs. ParseBridge.

Table extraction

A financial summary table from a quarterly earnings PDF.

Raw extraction

Revenue    Net Income   Growth
Q1 2024 $2.4M   15.3%  Q2
2024  $2.8M  $420K   Q3
2024 $3.1M$510K 10.7%
Q4   2024   $3.6M   $680K

ParseBridge output

| Quarter | Revenue | Net Income | Growth |
|---------|---------|------------|--------|
| Q1 2024 | $2.4M   | $340K      | 15.3%  |
| Q2 2024 | $2.8M   | $420K      | 16.7%  |
| Q3 2024 | $3.1M   | $510K      | 10.7%  |
| Q4 2024 | $3.6M   | $680K      | 16.1%  |

Document structure

Headings from an annual report -- the split points your chunking strategy depends on.

Raw extraction

Annual Report 2024
Financial Overview The company reported strong
growth across all segments. Revenue increased by
15% year-over-year. Operating Expenses Total
operating expenses were $12.4M, representing a
8% increase. Personnel costs remained the largest
component. Risk Factors Market volatility and
regulatory changes present ongoing challenges.

ParseBridge output

# Annual Report 2024

## Financial Overview

The company reported strong growth across all
segments. Revenue increased by 15% year-over-year.

## Operating Expenses

Total operating expenses were $12.4M, representing
an 8% increase. Personnel costs remained the
largest component.

## Risk Factors

Market volatility and regulatory changes present
ongoing challenges.

Purpose-built for document ingestion

Chunk-ready output

Markdown with preserved headings gives you natural split points. No regex hacks or post-processing scripts to get clean chunks.

Better retrieval accuracy

Structured content produces higher-quality embeddings. Tables stay as tables, lists stay as lists -- your vector search returns what users actually need.

Up to 300K pages/month

Parallel parsing engines keep your ingestion pipeline moving. Need more? Contact us for custom enterprise volumes.

Parse, chunk, embed -- three steps

ParseBridge gives you structured Markdown. Split on headings, generate embeddings, load into your vector store.

import OpenAI from "openai";

// 1. Parse PDF into structured Markdown
const res = await fetch("https://api.parsebridge.com/v1/parse/url", {
  method: "POST",
  headers: {
    Authorization: "Bearer pb_your_api_key",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    url: "https://example.com/annual-report.pdf",
  }),
});
const { markdown } = await res.json();

// 2. Chunk by headings — natural split points preserved
const chunks = markdown
  .split(/(?=^#{1,3} )/m)
  .filter((c) => c.trim().length > 0);

// 3. Embed each chunk and store
const openai = new OpenAI();
for (const chunk of chunks) {
  const { data } = await openai.embeddings.create({
    model: "text-embedding-3-small",
    input: chunk,
  });
  await vectorDb.upsert({
    text: chunk,
    embedding: data[0].embedding,
  });
}

Try It Free

50 free pages, no credit card required. View the API docs