Scrape anything public.
Break nothing.
Describe the data once. AI-Nexus Radar creates or reuses a Bright Data Scraper Studio collector, runs one URL or a batch, validates the output, and gives you a self-healing repair loop when extraction drifts.
Run a scraper to score completeness, row count and schema stability.
Bhuvan Jatav
Student Developer · AI & Web Innovation
Built and engineered by Bhuvan Jatav for the Into the Scrape-Verse hackathon.
Build the scraper around the problem, not the website.
Start with a new collector for a new target, or reuse an existing collector for repeat runs.
Ready. Configure a target and press Create & run scraper.
Your output. Your schema. No hardcoded site.
The result table is generated from the fields you ask for. Switch between table and raw JSON, export the dataset, then explore it visually.
| Run a scraper to populate results. |
Prove the scraper is healthy — then prove it can recover.
Audit completeness, simulate extraction drift, inspect the failure, and hand the affected fields to the self-heal workflow.
Field completeness
30% drift thresholdBreak it. Explain the failure. Repair it.
Keep the collector ID, describe the extraction failure in plain language, review the proposed repair, approve it, and re-run verification.
No proposed repair yet.
Designed around the hackathon criteria.
Turns arbitrary public web data into reusable structured-data products.
Natural-language schema + extraction health + repair workflow in one workspace.
Async-safe collector runs, retries, validation and exports.
Collector creation, execution, healing and approval all use Bright Data.
Null-rate and schema checks surface silent breakage before downstream users trust it.
The demo follows create → run → break → heal → verify.