Services · Web Scraping
Web Scraping Service.Built to run without getting blocked.
We build scrapers that handle JavaScript rendering, login walls, pagination, and anti-bot measures, and keep running in production without babysitting.
Fixed-price builds. Code in your GitHub from day one.
→ github.com/hassan173199640+ hrs
manual research eliminated per client
6 months
production runtime without manual intervention
5.0
avg rating, 17 verified client reviews
What we handle
JavaScript SPAs
Full Playwright/Chromium execution. Handles React, Vue, Angular, whatever the target renders.
Anti-Detection
We deal with Cloudflare, Akamai, PerimeterX, and DataDome. Each one needs different handling. Stealth profiles, human-like timing, canvas/WebGL spoofing, per-session fingerprint rotation. The first scraper usually fails. We tune until it doesn't.
Proxy Rotation
Residential and datacenter pool management. Automatic rotation on ban or rate-limit signals.
Scheduled Crawls
Sub-minute to weekly. Cron-triggered containers with automatic retry and dead-letter queues.
Change Detection
Every run compares against the last. Only new or changed records flow downstream, so your database doesn't fill up with duplicates and your ML models don't reprocess data that hasn't moved.
Structured Output
JSON, CSV, PostgreSQL inserts, S3 parquet, or webhook. Exactly what the next stage expects.
Need data from a specific site?
Tell us the target and the data you need. We scope it after a free call.
Stack
How the code looks.
async def scrape_listings(search_url: str) -> list[BusinessListing]:
browser = await playwright.chromium.launch(headless=True)
page = await browser.new_page()
await stealth(page)
await page.goto(search_url, wait_until="networkidle")
listings = []
while True:
items = await page.query_selector_all(".listing-card")
for item in items:
listings.append(BusinessListing(
title=await item.text_content(".listing-title"),
asking_price=parse_currency(await item.text_content(".price")),
cash_flow=parse_currency(await item.text_content(".cash-flow")),
revenue=parse_currency(await item.text_content(".revenue")),
location=await item.text_content(".location"),
industry=await item.text_content(".category"),
))
next_btn = await page.query_selector(".next-page")
if not next_btn:
break
await next_btn.click()
await page.wait_for_load_state("networkidle")
await browser.close()
return deduplicate(listings)await stealth(page)
Patches browser fingerprints (navigator, canvas, WebGL, and audio) before any requests are made, preventing bot-detection from profiling the session.
parse_currency(await item.text_content(...))
Currency strings from listing pages arrive inconsistently: '$1.2M', '$1,200,000', 'Asking: 1.2m'. parse_currency normalizes all formats to a canonical integer before storage.
deduplicate(listings)
Cross-page and cross-run deduplication by listing URL and title hash. Prevents duplicate records when pagination overlaps or listings reappear after a broker update.
Scraping is stage one.
Raw data by itself isn't useful. Every scraping job we build feeds directly into ML classification and workflow automation. One continuous pipeline, not stitched-together scripts.
17 reviews5.0 avg100% Job Success on Upwork
From clients
Top Rated Plus · 100% Job Success · $50K+ earnedExceptionally skilled back-end developer. Deep technical expertise in refactoring complex systems and building scalable multi-tenant architectures. Responsive, proactive, and consistently delivered above expectations.
Turki Alelyani
Founder, Feelix AI LLC, United States
Professional, responsive, and clearly committed to high quality work. Asked smart questions up front, provided progress updates without being asked, and delivered exactly what I needed on time.
Steven Cohen
GreenMark Consulting Group, United States
Hassan is responsive, detail-oriented, and thorough. He introduced AI combined with telecom into our projects and the results have been strong.
Sean Kannegiesser
IT / MSP Manager, Canada
2M+
pages scraped per day across active clients
0.3%
block rate on anti-detection scrapers
99.7%
field extraction accuracy on structured targets
Specific scraping services
Price Monitoring
Track competitor prices across Amazon, Shopify, and custom stores
Lead Enrichment
Scrape contact data from directories and score leads with ML
AI Training Data
Collect and curate datasets for LLM fine-tuning and RAG
Real Estate Data
Listings, pricing, and market data from Zillow, Redfin, Airbnb
Maps & Directories
Business data from Google Maps, Yelp, and industry directories
Social Media Monitoring
Reviews, mentions, and sentiment across forums and review sites
From the blog
Need a custom scraper?
Book a 30-min call or email us at contact@creativecodes.co