AI-powered Scraping & Web Data
LLM-assisted scrapers with proxies, dedup, and warehouse sinks.
LLM-assisted extractors that survive layout changes — managed proxies, fingerprinting, dedup, sinks into BigQuery/Postgres/CRM.
Problems I solve
- Scrapers break every layout change
- Blocked by anti-bot systems
- Data arrives dirty and unstructured
- No pipeline from scrape → warehouse
What you get
- LLM-assisted structured extraction
- Managed proxies + browser fingerprinting
- Dedup, normalization, enrichment
- Direct sinks to warehouse / CRM
Use cases
Competitor pricing tracker
Daily price scrape + change alerts across 20+ competitor SKUs.
Lead sourcing pipeline
Directory → enriched → deduped → HubSpot with intent signals.
Content monitoring
Track publications, releases, or reviews with LLM summaries.
Marketplace / listing intelligence
Structured listing data landed in your warehouse for analytics.
Examples I've shipped
Competitor pricing radar
Outcome — Detected 40+ competitor pricing moves in the first month.
Sales prospect firehose
Outcome — 600 qualified prospects/month at 1/4 the cost of a vendor.
How I work
- 1
Target scoping
Sources, fields, cadence, and legal considerations.
- 2
Extractor build
Playwright + LLM extractor with schema validation.
- 3
Infra
Proxies, retries, dedup, warehouse sinks.
- 4
Monitor
Change detection + quality dashboards.
Deliverables
- Scraper + pipeline
- Structured schema
- Warehouse / CRM sink
- Monitoring
Benefits
- Survives layout drift
- Data ready to use, not to clean
- No CSV middleman
Frequently asked questions
Is this legal?+
I only scrape publicly accessible pages, respect robots.txt / ToS where they apply, and advise on legal review for anything sensitive.