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PyronetsPyronets
Product Intelligence

Product Data From Any E-commerce Source

Complete product datasets (names, SKUs, specifications, prices, images, reviews, and availability) extracted from any retailer, marketplace, or brand site at scale.

Catalogue depth

What a product record carries beyond a name and a price.

Variant-level
Size, colour and SKU resolved as separate rows
Stock state
Availability captured in the same observation as price
Media & specs
Image URLs and attribute tables carried through
See the receipts
The problem

Building a comprehensive product catalog from multiple sources is grueling work

Product data is inconsistently structured across every retailer, manufacturer, and marketplace. Collecting, standardizing, and maintaining it at the SKU level requires engineering and data operations that most businesses cannot staff internally.

  • Product attributes, field names, and formats vary dramatically across every website and platform
  • Matching products across multiple sources requires entity resolution that simple string matching cannot handle
  • Specification data is buried in unstructured HTML tables with inconsistent headings and units
  • Images, pricing, and availability change frequently and require continuous re-collection
  • Review content is distributed across pages and often requires JavaScript rendering to access
Our approach

Comprehensive product data, structured and delivered on demand

Pyronets extracts complete product records from any source with normalized attributes, matched specifications, cleaned descriptions, and all associated media URLs, delivered as a ready-to-use catalog dataset.

What you get instead

Structured records in your schema, not raw HTML
Extraction repaired by us when sources change
Delivery on a fixed schedule, with a QA report attached
Scope this dataset

What you get

Full Catalog Extraction

Extract complete product catalogs from any e-commerce site (category pages, search results, brand pages, and individual product detail pages) with full coverage across all SKUs.

Specification & Attribute Parsing

Technical specifications from HTML tables, feature lists, and unstructured text are parsed into named key-value pairs with normalized units and consistent field naming.

Product Image Collection

All product image URLs (hero images, gallery shots, lifestyle images, and variant images) are collected and organized by product. Bulk image download available on request.

Reviews & Ratings Extraction

Consumer reviews (star rating, review text, reviewer name, verified status, helpful votes, and date) collected at scale including paginated review sets.

Product Matching & Enrichment

Products from multiple sources are matched by EAN, UPC, ASIN, MPN, or fuzzy logic. Matched records are merged to produce enriched product profiles from all available sources.

Live Availability & Price Monitoring

Prices and stock status are monitored at configurable frequencies. Changes are flagged in the dataset so your team always knows what has updated since the last delivery.

Schema

Fields we collect.

A starting point, not a fixed list, the final schema is whatever you sign off on during scoping.

Product Name
Exact product title as listed on the source
SKU / GTIN / ASIN
All available product identifiers
Brand
Brand name extracted or normalized from listing data
Category Path
Full breadcrumb category hierarchy from the site
Description
Full cleaned product description text
Specifications
Parsed key-value attribute pairs from spec tables
Price
Current selling price with currency code
Availability
In stock, out of stock, pre-order, discontinued
Image URLs
All product image URLs including gallery images
Average Rating
Aggregate star rating from all collected reviews
Review Count
Total number of reviews collected for the product
Seller Data
Seller name and fulfillment type for marketplace listings
Process

How your pipeline gets built

From first conversation to recurring delivery, with a sample you sign off on before anything runs on a schedule.

01

Define Sources & Fields

Specify which retailers, brands, or marketplaces to scrape and which product fields you need. We assess coverage and feasibility for each source and confirm the output schema.

02

Scraper & Extractor Build

Purpose-built scrapers and field extractors are developed for each source, including specification parsers, review paginators, and variant-level handling.

03

Sample & QA Review

A representative sample covering all sources is delivered. You validate field coverage, attribute naming, review quality, and image URL accuracy before full extraction.

04

Full Delivery & Refresh Cadence

Complete catalog extraction is delivered along with your chosen refresh schedule for prices, availability, and new product additions going forward.

Applications

Common use cases.

01

Catalog Enrichment

Fill gaps in your internal product catalog using data collected from manufacturer sites, distributor portals, and competitor listings: descriptions, specs, and images included.

02

Competitive Catalog Analysis

Map competitor product ranges, identify gaps in your assortment, and understand how your catalog overlaps or differs from key competitors across categories.

03

Marketplace Listing Optimization

Benchmark your Amazon, eBay, or Walmart listings against top-performing competitor product pages to identify description, image, and attribute improvements.

04

Review & Sentiment Analysis

Collect product reviews at scale from multiple platforms to analyze customer sentiment, identify recurring complaints, and benchmark review volume against competitors.

05

Price Intelligence

Monitor how product prices evolve across retailers over time. Understand pricing dynamics, seasonal patterns, and promotional depth at the SKU level.

06

Private Label Research

Analyze best-selling products, review sentiment, pricing sweet spots, and specification standards in a category to inform private label product development decisions.

Delivery formats

Pick whatever drops straight into your stack.

CSVExcel (XLSX)JSONXMLSFTPREST APIBigQueryDatabase Insert

Industries served

Where this dataset tends to be used.

E-commerce & RetailConsumer ElectronicsFashion & ApparelHome & GardenHealth & BeautySporting GoodsB2B DistributionManufacturing & Wholesale
Quality

Validated before it reaches you.

Every batch runs the full validation suite before delivery. If a batch fails, it does not ship. We investigate and repair the extraction first, and you get told what happened.

Type conformance
Every field matches its declared type
Range and sanity
Values fall inside agreed bounds
Null-rate thresholds
Coverage flagged when it drops
Duplicate resolution
Resolved against your chosen key
Schema drift
Source structure changes caught early
Volume variance
Unexpected record counts halt delivery
Questions

Frequently asked questions

Yes. We use headless browser automation for JavaScript-rendered storefronts. Product data loaded dynamically via API calls is also captured directly from network requests where accessible.

Yes. We collect variant-level data including all available size, color, material, and configuration options, along with variant-specific prices, availability, and images.

We match using GTIN/EAN/UPC barcodes or manufacturer part numbers as primary keys, with fuzzy name and description matching as a fallback. Match confidence scores are included in output.

We paginate through all available review pages. There is no per-product limit. For products with tens of thousands of reviews, full collection is feasible and all reviews are delivered in the dataset.

Yes. Bulk image downloading and delivery via SFTP or cloud storage is available as an add-on. Images can be renamed by SKU or GTIN for easy integration with your product systems.

Tell us the sources. We'll send back a sample.

Describe the sites and the fields you need. You'll get a proposed schema, a real sample extracted from those sources, and a fixed price, before any commitment.

No retainer required to find out whether your sources are feasible.

Sectors this data serves

Sectors this dataset commonly serves. Schema and cadence are set per project.

E-commerce PlatformsPrice Comparison SitesBrand MonitoringMarketplace AnalyticsERP & PIM SystemsRetail AnalyticsConsumer GoodsManufacturing

Sample output

An illustrative preview of delivered records. The real schema is whatever you sign off on.

sample_output.csv
skuproduct_namepriceavailabilityratingretailer
WNC-4421Wireless Noise-Canceling Headphones$89.99In Stock4.7 (2,341)Amazon
OFC-9982Ergonomic Office Chair Pro$449.00Low Stock4.5 (891)Wayfair
SW-7731Smart Watch Series 9$329.99In Stock4.8 (5,120)BestBuy
3 of 1,512,440 records shown · CSV · Delivered daily at 06:00 UTC