We extract product specifications, daily price fluctuations, retailer offers, and store ratings from PriceSpy. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Details objects from pricespy.co.uk. All fields typed and schema-versioned.
"product_id": "5873912", "name": "Apple iPhone 15 Pro 128GB", "brand": "Apple", "lowest_price": 899.0, "highest_price": 1099.0, "rating": 4.7, "review_count": 312, "release_date": "2023-09-12"
| # | product_id | name | brand | category | sub_category | lowest_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Retailer Offers objects from pricespy.co.uk. All fields typed and schema-versioned.
"product_id": "5873912", "retailer_name": "Amazon UK", "price": 899.0, "shipping_cost": 0.0, "total_price": 899.0, "stock_status": "In stock", "store_rating": 4.8, "offer_condition": "New"
| # | product_id | retailer_name | retailer_url | price | shipping_cost | total_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Price History objects from pricespy.co.uk. All fields typed and schema-versioned.
"product_id": "5873912", "date": "2026-05-12", "lowest_price": 899.0, "average_price": 945.5, "price_drop_pct": 5.2, "retailer_count": 24, "historical_high": 1099.0
| # | product_id | date | lowest_price | average_price | price_drop_pct | retailer_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Intelligence objects from pricespy.co.uk. All fields typed and schema-versioned.
"store_id": "8492", "store_name": "Currys", "average_rating": 4.2, "review_count": 15430, "positive_pct": 82, "active_offers": 4192, "return_policy": "21 days"
| # | store_id | store_name | average_rating | review_count | positive_pct | neutral_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from pricespy.co.uk. All fields typed and schema-versioned.
"keyword": "oled tv 65 inch", "position": 1, "product_id": "719384", "name": "LG OLED65C3", "lowest_price": 1499.0, "store_count": 18, "rating": 4.9, "scraped_at": "2026-05-12T10:15:00Z"
| # | keyword | position | product_id | name | lowest_price | store_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our PriceSpy scraper handles every layer of the platform: product specifications, dynamic pricing tables, historical charts, and store ratings, with JavaScript rendering and anti-bot circumvention built in.
Title, brand, category taxonomy, images, and deep technical specifications scraped at the product level.
Capture price, shipping cost, total cost, stock status, and delivery estimates for every retailer listed on a product.
Extract historical price curves to track discounting trends, lowest recorded prices, and seasonal fluctuations.
Aggregate store performance data including average ratings, review counts, and customer sentiment distribution.
Monitor inventory indicators across multiple retailers to identify supply chain constraints and out-of-stock patterns.
Track product visibility and ranking positions for specific keywords and category filters.
Extract data across PriceSpy regional domains to compare international pricing strategies.
Parse complex delivery matrices to calculate true landed costs for consumer electronics.
Run one-off bulk exports or configure continuous pipelines at daily or hourly cadences with change-detection.
Brief in. Clean data out.
Provide product URLs, category links, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for pricespy.co.uk.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Price comparison sites rely on complex front-end rendering and aggressive rate limiting. Here is how we maintain stable extraction.
PriceSpy employs aggressive rate limiting and bot detection. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain access.
Retailer offer tables and price history charts are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution to capture data that headless HTTP clients miss entirely.
Front-end frameworks change frequently. Our selector strategy uses multiple fallback chains per field, including Next.js hydration state extraction, so a layout change does not break your data pipeline.
For large product catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops, responding before you notice.
Retailers monitor competitor pricing across the market to automatically adjust their own prices and maintain competitiveness.
Brands track how their products are priced across different retail channels to identify MAP violations and unauthorised discounting.
Analysts track category pricing trends, new product entries, and feature standardisation across consumer electronics.
Brands monitor store ratings and stock availability to evaluate the performance of their retail partners.
Category managers analyse market gaps and competitor assortments to optimise their own product offerings.
Marketing teams track historical price drops to anticipate competitor sales events and optimise promotional calendars.
"PriceSpy aggregates the entire retail market into a single view, but extracting that pricing matrix requires a dedicated infrastructure team."
Most engineering teams underestimate the complexity of scraping comparison engines. Reliable PriceSpy extraction requires residential proxies, full JavaScript rendering for dynamic offer lists, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your developers can focus on pricing strategy.
Everything supported by our pricespy.co.uk scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and retry logic. Playwright handles JavaScript rendering and interaction flows for dynamic pricing tables.
We maintain pools of residential ISP proxies across UK regions. Rotation happens per-request to prevent IP bans and rate limiting.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About pricespy.co.uk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and product information from PriceSpy is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate limits in real time and trigger pool rotation automatically.
Yes. We extract the historical price data points used to render the price charts on product pages, giving you visibility into past discounting trends.
Real-time streaming pipelines achieve sub-60-minute latency for specific product sets. Full category refreshes at daily cadence complete within a 4-8 hour window depending on scale.
Yes. We extract the base price, the shipping cost, and the total landed cost for each retailer listed on a product page.
Yes. We capture the exact stock status string provided by PriceSpy for each retailer offer, allowing you to monitor inventory availability.
Our smallest packages start at a defined product list of 5,000 items with daily delivery. For larger catalogues, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 products as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off category dump or a continuous price-monitoring feed across 100K products, we scope, build, and operate the pipeline. Tell us what you need.
