Turn messy websites into clean, agent-ready data
Lossless markdown parsing, structured JSON schemas, and objective-driven DOM compression from any URL—including JS apps and multi-page PDFs.
from parallel import Parallel
client = Parallel(api_key="PARALLEL_API_KEY")
# Extract objective-aligned markdown from any complex URL
doc = client.extract(
url="https://investor.nvidia.com/financial-reports/default.aspx",
objective="Extract Blackwell data center GPU revenue, gross margin, and CapEx guidance",
format="markdown" # markdown | json | text
)
print(f"Title: {doc.title}")
print(f"Clean Markdown Excerpt (90% token reduction):\n{doc.content}")Engineered for dense web documents
JavaScript & SPA Rendering
Headless Chromium execution captures React, Vue, and Angular applications with infinite scroll and dynamic hydration.
Multi-Page PDF Parsing
Extract clean tables, financial footnotes, and academic citations directly from multi-page PDFs with visual coordinate mapping.
Objective-Driven Pruning
Strip out navigation menus, ads, footers, and sidebars, delivering only the precise text relevant to your agent's query.
Structured Schema Extraction
Pass a JSON Schema or Pydantic model to extract strongly typed objects directly from unstructured web copy.
Built for enterprise, secure by design
SOC 2 Type II certified, GDPR compliant, and strict Zero Data Retention (ZDR) guarantees. Your confidential queries and internal data never touch model training.