AI Website-to-Dataset
Pricing
from $30.00 / 1,000 page extracteds
AI Website-to-Dataset
Website-to-JSON scraper you can trust. Turn any website into structured data matching the schema you define, with type validation, coercion and missing-field=null for reliable, predictable extraction every run.
Pricing
from $30.00 / 1,000 page extracteds
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Turgay NANTA
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AI Website Data Extractor — Reliable JSON from Your Schema
Turn any web page into structured JSON that matches the schema you define — without writing a custom scraper. An LLM reads the page and extracts the fields you ask for; we guarantee the output is reliable.
What it does
Instead of hand-scraping information from a page (product name, price, stock, rating…), you write the fields you want and their types, and the actor returns them as clean JSON. Review-scrapers and downstream pipelines can consume the output directly.
What makes it different
Most AI scrapers say "describe your fields in plain language" but never document type validation, coercion, or missing-field behavior — so sometimes a number comes back as a string, and sometimes a field silently disappears. We close that gap:
- Type coercion:
"$1,299.90"→1299.9,"yes"→true,"a, b, c"→ a list. EU/US decimal separators and currency/unit symbols are cleaned automatically. - Strict schema: The output contains ONLY the fields in your schema, in the correct type. Extra fields outside the schema are dropped (predictable output).
- Missing = explicit
null(no silent failure): A field that isn't on the page is not skipped; it's written asnull. If a required field is missing, the record is flagged with_schema_valid = false. - Reliability proof: At the end of every run you get a per-field quality summary (ok / coerced / missing / required_missing) plus a validity rate.
- Grounding (anti-hallucination) flag: Each record marks every filled value by whether it was found on the source page (
high= verified on the page,unverified= not found / possible invention,filled= boolean/array/object,null= empty). This is a deterministic text/number match — not proof, but it makes likely inventions visible.
Output language
Set Output language to choose the language for text-field values. The LLM is instructed to write — and if needed translate — every natural-language value into that language, even when the source page is in another language. JSON keys (your field names), numbers, and booleans are unaffected.
Input
| Field | Description |
|---|---|
urls | The pages to extract from (each becomes a separate record). url / startUrls are also accepted |
schema | Field names + types. {"title":"string","price":"number"} or ["title","price:number"] or [{"name":"rating","type":"number","required":true}]. Types: string|number|integer|boolean|array|object |
language | The language for text (string) field values (default: English) |
model | (Advanced) the LLM model |
Output (dataset)
One record per URL: the schema fields (correctly typed) plus _url, _schema_valid, _field_status (per-field diagnosis), and _confidence (per-field grounding flag). A final summary row carries page_count, schema_valid_records, validity_rate, and field_quality.
Pricing (Pay-Per-Event)
You pay only for what you use: per run start, per page, and per validated field. No monthly subscription.
