SSiddiq.in
Scraping · Extraction

The data you need, cleanly.

Competitor pricing every hour. Marketplace product feeds. Lead lists for a niche vertical. Public-record aggregation. We build the scrapers and pipelines that keep running.

$ pipeline run competitor-pricing
→ launching 12 headless workers
→ visiting 1,842 product URLs
→ retries: 34 · captchas solved: 8
→ AI-extracting price + stock…
✔ 1,809 records → postgres.prices
→ diff vs yesterday: 214 changes
✔ slack alert sent (14 undercuts)

What we harvest.

Competitor monitoring

Prices, stock, promos, new SKUs — as often as you like.

Lead generation

Niche directories, industry lists, LinkedIn (compliant).

Catalogue aggregation

Consolidate supplier feeds into one clean product DB.

AI-powered extraction

Unstructured pages → structured JSON via GPT with schema.

Built to keep running.

Scrapers rot the day a target updates their DOM. We architect for that day.

  • Residential proxy pool
  • Playwright + Puppeteer
  • CAPTCHA solving
  • Auto-retry with backoff
  • Change detection & diffs
  • Rate limiting per domain
  • Alerting on breakage
  • Legal review per target

Extraction FAQs.

We only scrape public data, respect robots.txt when required, throttle politely, and avoid data behind auth or paywalls unless licensed. We won't touch anything grey.

Source strategy — pick the least fragile method

Scraping is the last resort. Fifteen years of extraction work says: use the ladder in this order.

Source typeFirst choiceFallbackLegal note
Structured public dataOfficial APIBulk downloadRead the ToS; most allow
Vendor dataPartner API + contractExport cronContract governs, not scraping law
Public web (JS-light)HTTP + parserHeadless browserRespect robots.txt + rate limits
Public web (SPA-heavy)Playwright / PuppeteerReverse-engineer XHRXHR reveal is often cleaner
Behind loginHuman-assisted + sessionManual exportNever share creds across accounts
PDF / scansLayout-aware OCRLLM-vision extractionSee document-ai for pipeline

Data quality guards that catch silent breakage

Extractions rot silently. The site adds a class, a field moves, and you don't notice for weeks. These guards catch it fast.

  • Schema validation on every run

    Zod/pydantic schema per source; run fails loudly if a field disappears or changes type.

  • Row-count deltas

    Alert if today's extraction is <70% or >150% of trailing 7-day average — usually means source changed.

  • Null-rate monitoring

    Per-field null rate tracked; sudden jump = selector broke. More sensitive than schema checks.

  • Distribution drift

    For key numeric fields, alert if mean/median shifts more than 2 sigma between runs.

  • Golden record test

    5-10 hand-verified records rerun daily; the extraction pipeline fails CI if any differ.

  • Poison-record detection

    Common failure: page returns 200 with an error banner. We fingerprint known error templates and reject.

Turn the internet into a database.

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