Case Studies
These examples show the kinds of problems we solve and how a typical project unfolds — from first challenge to a working data pipeline — across pricing, marketplaces, grocery and brand intelligence.
How US businesses use our web data
Each example follows the same shape — the challenge, what we built, and the outcome.
Weekly Grocery Pricing Across 10 US Retail Chains
A meal-planning app needed weekly promotional prices for 500 ingredients, including loyalty offers and purchase limits.
A circular scraping pipeline with ingredient matching, ZIP-store mapping, and promotional price extraction.
The platform enabled weekly price comparisons and optimized grocery baskets across multiple retail chains.
Store-Level Wine Data Across 15+ US Retail Chains
A US winery needed daily store-level pricing and distribution visibility for 8 wine SKUs across 15+ retail chains.
A UPC-based pipeline capturing store-level availability, pricing, promotions, and case deals.
The winery gained daily distribution visibility, pricing insights, and distribution-gap detection across retail locations.
ZIP-Level Grocery Pricing Across 32 US Retailers
A grocery comparison app needed accurate store-level prices by ZIP code, with UPC-matched products and separate promotional pricing.
A normalized price feed with ZIP-to-store mapping, UPC matching, daily refreshes, and API delivery.
The app received data across 32 retailers, 12,400 SKUs, and 15,600 ZIP codes with 98.6% audited match accuracy.
Recurring Apparel Data Across 5 US Department Stores
A federal contractor needed recurring apparel pricing and product data across five department stores and six categories.
A scheduled scraping pipeline capturing 15+ fields with quality checks and secure SFTP delivery.
The project achieved 100% on-time delivery during the base period with zero missed collection windows.
REAL-TIME MARKETPLACE PRODUCT INTELLIGENCE
An AI shopping platform needed fresh product, pricing, and availability data across major US marketplaces.
A multi-marketplace pipeline with AI product matching, structured specifications, and tiered refresh cycles.
The platform received fresh product intelligence for live shopping results and AI-powered recommendations.
E-COMMERCE & MARKETPLACE INTELLIGENCE
A US outdoor equipment reseller needed reliable competitor pricing across 700 SKUs, but manual monitoring was too slow and inconsistent product matching led to incorrect repricing decisions. The team needed hourly visibility into competitor prices, sellers, Buy Box status, and availability.
An AI-powered marketplace price monitoring pipeline with verified product matching, hourly data collection, delta detection, and repricing-ready API delivery across Amazon, Walmart, and eBay. Match-confidence and pricing guardrails ensured unreliable competitor offers were excluded from automated decisions.
Price-change detection improved from 2–4 days to under one hour, audited product-match accuracy reached 98.4%, and Buy Box win rate increased by 31%. The system also recorded zero false-match price cuts and maintained 99.9% uptime.
The pattern behind every engagement
Typical time from scope to a first validated pilot dataset.
Consistent schema, however many sources a project covers.
Clients work directly with the engineers building the pipeline.
Most pilots become a maintained, monitored data feed.
From first conversation to live data
Every example above followed this same straightforward path.
Scope the challenge
We define the problem, target sites and the fields you need.
Pilot dataset
We build and deliver a validated sample in 3–7 days.
Refine & approve
We adjust the schema and coverage until it fits.
Ongoing feed
The pilot becomes a maintained, monitored pipeline.
About these case studies
The examples on this page describe realistic project types based on the kinds of work we do. Client names and specific figures are kept anonymous to protect confidentiality unless a client has agreed to be named.
Where clients permit, we can discuss relevant examples for your industry on a call. Contact us and tell us your sector so we can share the most relevant context.
Most projects begin with a pilot dataset delivered within 3 to 7 days, followed by an ongoing feed or managed pipeline once the approach is validated.
Contact us with your target sites and the fields you need. We will scope the work and return a sample dataset so you can evaluate quality before committing.
Make your project the next example
Tell us the challenge you're facing and we'll return a sample dataset within 1 business day.