Crawlbase can collect a lot of web data, but collection is only the first step. Strawberry helps you compare results with the research question, preserve source URLs, investigate coverage gaps, and produce a quality review that your team can verify.
01
Measure coverage against the real target list.
A large crawl may still miss the domains, attributes, or pages a project actually needs.
Strawberry can compare the visible Crawlbase output with your target list and expose the gaps clearly.
02
Keep each finding tied to its source page.
Research gets hard to audit once the claim becomes separated from its page.
Ask for a structured output that retains the URL, observed fact, context, and uncertainty needed by a reviewer.
03
Sort imperfect results into a usable review queue.
Blocked requests, duplicates, stale pages, and blank fields require different next steps.
A companion can categorize those issues from the available result set and explain what further collection would need.
04
Protect the checks when research becomes recurring.
Before each Crawlbase collection run, check the target universe, request failures, duplicate rows, and coverage against the research question, then prepare an exceptions brief. Publishing an unchecked scrape can turn incomplete coverage or stale records into a claim people treat as evidence.
Open the signed-in Crawlbase dashboard with the relevant crawler, API response, or extraction job visible so a companion can help review the web-data work in context.
It can use the visible Crawlbase crawl results, extraction settings, and source pages to prepare a reviewable data-quality queue, crawl brief, or collection handoff.
Approval is required before an action changes a record, sends or posts something, or spends money.
Yes. Put a Crawlbase collection-quality pass ahead of every research run to check scope, failed requests, duplicates, and coverage. Review any re-collection or published finding against the underlying rows first.
Crawlbase Strawberry