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Web Fusion Data
AI-powered extraction · REST API · JSON
Skip the CSS selectors and regex. Describe what you want in plain language or a schema, point the AI parser at a URL, HTML blob or document, and get back clean structured JSON — resilient to layout changes that would normally break a traditional scraper.
# 01 · Overview
An AI parser is an extraction engine that uses a language model to read unstructured content — a web page, a PDF, a scanned document, a raw text blob — and turn it into structured, machine-readable data. Instead of writing brittle CSS selectors or regex patterns that break the moment a site redesigns a page, you describe what you want in natural language or a reusable schema, and the model figures out where that data lives on the page. It's AI-powered data extraction as a service, built for teams who want structured output without maintaining parsing logic by hand.
Submit a URL, raw HTML, a document file, or plain text along with your API key.
Describe fields in natural language, or pass a reusable JSON schema for structured output.
A language model interprets the page or document structure and locates each requested field.
Extracted values are type-checked against your schema and scored for confidence.
You get structured output synchronously, via webhook, or in a bulk export.
# 02 · Capabilities
Built to extract structured data reliably, even when the source content is messy, inconsistent or changes without warning.
Describe the fields you want in plain English instead of writing extraction logic.
Define a JSON schema once and reuse it across thousands of pages or documents.
No CSS selectors to maintain — the model adapts automatically when page layouts change.
Extract structured fields from PDFs, scanned documents and screenshots with built-in OCR.
Every extracted field is returned with a confidence score so you can flag low-certainty output.
Parse content across languages and scripts without separate pipelines per locale.
Run single parses synchronously, or batch thousands of inputs asynchronously.
Automated schema validation catches malformed or incomplete extractions before delivery.
# 04 · Industries
From retail catalogs to legal contracts, teams across every industry use the AI parser to turn unstructured content into data they can actually query.
# 05 · API reference
A small, predictable set of REST endpoints covers single parses, document parsing, schema management and batch jobs.
# 06 · Example
Every response follows the same predictable JSON structure, regardless of the input format or source.
# 07 · Access
Every account gets a bearer-token API key, a sandbox environment for testing, and clear, predictable limits so you can plan capacity with confidence.
Every request is authenticated with an API key passed in the Authorization header.
Per-minute request limits scale with your plan, with clear headers on remaining quota.
Test schemas and instructions against sample inputs before spending live parse credits.
# 08 · Monitoring
Every account includes a dashboard showing parse volume, confidence scores, latency and job status in real time — so you can watch extraction quality and debug integrations without digging through logs.
# 09 · Schema
The extracted fields themselves are defined by you — but every response wraps them in a consistent envelope.
# 10 · Integration
Integrate however fits your stack — call the REST API directly, use a client library, or push results straight into storage.
Lightweight libraries for common languages wrap authentication and schema management for you.
Register a callback URL and get pushed structured output the moment a batch job completes.
Pipe extracted data directly into spreadsheets, BI tools or automation platforms without writing code.
JSON, CSV or XLSX exports for batch jobs, delivered to S3, GCS or your own bucket.
Managed connectors write extracted results straight into your warehouse on a schedule.
Versioned reference docs, schema examples and changelogs for every endpoint.
# 11 · Infrastructure
The infrastructure that makes extraction reliable — from reaching the page to interpreting it correctly — runs entirely on our side, so your integration only ever deals with clean, validated output.
Rotating proxies and headless rendering fetch JavaScript-heavy pages before parsing begins.
Extraction accuracy is benchmarked against labeled test sets before any model update ships.
Low-confidence extractions can trigger an automatic re-parse before being returned to you.
# 12 · Applications
Replace brittle selector-based scrapers with extraction that survives redesigns.
Turn invoices, contracts and forms into structured records without manual entry.
Normalize messy feeds, exports and legacy data into one clean schema.
Let analysts define extraction rules in plain English instead of writing code.
Extract structured records from old reports and exports ahead of a system migration.
Pull structured line items and totals out of incoming email and billing documents.
Extract structured datasets from papers, reports and public filings at scale.
Bring HTML, PDF and spreadsheet sources into one consistent downstream schema.
# 13 · The case for us
A developer-first extraction engine for teams who need structured data from messy sources without hand-writing and maintaining parsing logic for every format and layout.
# 14 · Questions
A traditional scraper relies on CSS selectors tied to a page's exact HTML structure, so it breaks when a site redesigns. The AI parser uses a language model to understand the content itself, so it keeps extracting the fields you've defined even after layout changes.
No. Most extraction works zero-shot from a natural-language description or a JSON schema. You can optionally provide example inputs and outputs to improve accuracy on an unusual format.
Every field comes back with a confidence score, and average field-level confidence across accounts runs above 96%. Low-confidence extractions can be configured to trigger an automatic retry or a flag for manual review.
The underlying model handles a wide range of languages and scripts, so you can parse multilingual content without building separate pipelines per locale.
Input content is processed to generate your extraction and is not used to train shared models. Specific data-retention terms are available during onboarding for teams with compliance requirements.
Yes. Save a schema once through the /v1/schema endpoint and reference it on every subsequent parse request for consistent, structured output.
Yes, every account includes a sandbox environment with sample inputs so you can test schemas and instructions before spending live parse credits.