Structured extraction, grounded in your schema
Unstructured scrapes waste analyst time. Stark Crawl extracts the fields you define — company details, contacts, product attributes, and other prospect signals — so research output is ready to use.
Fields you define, not page dumps
Generic scrapes return markup and noise. Stark Crawl extracts against a schema you control — company name, city, category, email, product attributes — so analysts get columns they can filter and join.
If a page does not contain a field, the row stays honest about what is missing instead of inventing values.
Comparable rows across hosts
The same schema applies across accepted hosts, so a prospect from a German directory and a French listing land in matching columns.
That makes category coverage, competitor comparisons, and outreach lists practical without a separate normalisation step for every site.
Research-ready enough for real work
Structured captures are what make crawls safe to put in front of analysts mapping a vertical, sales ops building a list, or researchers monitoring competitors.
You stay in control of the source of truth: refine the schema, re-run, and export — nothing more, nothing less than the fields you asked for.
Define the fields that make a prospect useful and see structured rows fill in against your schema.
Try for freeRows you can analyse
Extract the fields you define from crawled pages — structured, on-schema, and ready to export.
