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About UsWho we are and what we do.AI Data Extraction for Sources That Don't Fit a Template
Some data doesn't live in a clean product page or a tidy API. When your source is messy, inconsistent, or one of a kind, we build the AI extraction around it.
When "Just Scrape It" Isn't Enough
Your source doesn't look like anything else you've scraped.
Every page, file, or record is laid out differently, so there is no single pattern for a scraper to follow.
The data is buried in inconsistent formats.
PDFs, scanned pages, mixed layouts, and free text all need a different kind of reading, not another selector.
You need judgment, not just extraction.
Classifying, tagging, or summarizing what is on a page takes more than pulling text out of HTML.
A one-off requirement doesn't justify a full pipeline.
You need this data, not a new team.
This is where AI earns its place. Not to replace scraping, but to handle what fixed rules cannot.
Extraction That Adapts to Your Source, Not the Other Way Around
We scope the exact fields, formats, and sources you need before writing a line of code.
Our models read pages the way a person would, using structure and context, not just tags.
Every output is checked against your schema before it reaches you.
What changes on the source side gets handled on ours.
How an AI Data Extraction Project Starts
Scope
A short call to walk through your sources, fields, and edge cases.
Sample
We extract a real batch from your actual sources so you can judge the output before anything is signed.
Build & validate
We tune the extraction to your schema and run it against your QA rules.
Deliver
Data lands in the format and destination you use, on your schedule.
Every AI extraction project is scoped to the sources, volume, and complexity involved, so we quote it after we understand it. Get a free sample first and decide with real output in hand.
Built for the Sources That Break Standard Scrapers
Structure-aware parsing
Extract clean fields from HTML, PDFs, scanned documents, or mixed content, even when the layout has no consistent pattern.
Extractive-first accuracy
We pull what's actually on the page first. Where interpretation is needed, like classification or summarization, it's flagged and scored, not guessed silently.
Self-adapting pipelines
When a source changes shape, our models re-read it, and our engineers check the result. You don't file a ticket to get your data back.
Human review before delivery
A person checks samples against your rules before delivery. You get validated data, not a raw model dump.
{
"document_type": "scanned_invoice",
"source_file": "vendor_upload_0421.pdf",
"extracted": {
"invoice_number": "INV-2024-8841",
"vendor_name": "Northwind Traders",
"total_amount": "1,240.00",
"currency": "USD"
},
"classification": { "label": "invoice", "confidence": 0.94 },
"flags": [
{ "field": "line_items", "note": "low_confidence", "confidence": 0.62 }
],
"human_reviewed": true
}Built With Compliance in Mind
Every extraction runs under the same governance as the rest of APISCRAPY, so your legal and security teams can sign off without a discovery exercise.
Sources handled within the data protection regulations relevant to your industry.
Provenance and handling documented for every source we extract from.
ISO 27001:2022 and ISO 9001 certified, CMMI Level 3 appraised, and GDPR compliant.
Encrypted in transit and at rest, with role-based access, NDAs, and full audit logs.
AI Data Extraction Questions, Answered
What is AI data extraction?
AI data extraction uses machine learning models to pull structured fields out of unstructured sources, such as web pages, PDFs, and scanned documents. It reads layout and context to find each field, so it works on sources where a fixed template or scraper would fail. The output is clean data in a schema you define.
How is this different from your web scraping services?
Standard scraping is for well-structured, catalog-style sources like ecommerce listings or real estate feeds. AI Data Extraction is for messy, inconsistent, or custom sources that don't fit a template.
Will the AI make things up?
We design against it. We extract what is actually present first. Where judgment is required, we flag it and score our confidence instead of presenting a guess as fact, and a person reviews samples before delivery.
How accurate is it?
Accuracy depends on the source. Every field gets a confidence score, low-confidence fields are flagged for review, and a person checks samples against your rules before delivery. Your free sample shows the accuracy on your own data before you commit.
What kinds of sources can you handle?
Web pages, PDFs, scanned documents, and mixed-format sources. If you're not sure yours qualifies, send it over and we'll tell you plainly.
How long does a project take to start?
Most projects move from scoping call to a sample dataset within days.
Is my data handled securely?
Yes. We work under ISO 27001:2022-certified security and CMMI Level 3-appraised processes, with GDPR-compliant handling, the same as the rest of APISCRAPY.