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.
Each example follows the same shape — the challenge, what we built, and the outcome.
A premium Swiss sportswear brand struggled to enforce MAP policies across Amazon, Zalando, Decathlon, and other marketplaces due to dynamic pricing, currency fluctuations, and complex product variant tracking.
An automated marketplace monitoring solution that tracked prices, seller activity, stock levels, and SKU variants across multiple European marketplaces, delivering daily MAP compliance reports and violation alerts.
The brand identified unauthorized sellers faster, reduced pricing leakage by 92%, improved marketplace compliance, and protected margins across cross-border distribution channels.
A leading FMCG brand struggled to track rapidly changing prices, discounts, and inventory across Q-commerce platforms where localized pricing, stock availability, and promotions changed multiple times a day.
A high-frequency Q-commerce data collection API capable of processing 50,000+ daily queries across Zepto, Blinkit, and Swiggy Instamart, delivering real-time pricing, discount, and dark-store inventory intelligence with low-latency data feeds.
The client achieved 99.7% data delivery reliability, reduced data latency to under 8 minutes, gained instant visibility into competitor pricing and stockouts, and improved digital shelf conversions by 14%.
Read how a premium supermarket system integrated store-level grocery data scraping by WebDataScraping to monitor app pricing matrices by ZIP code.
A hyperlocal pricing intelligence feed that captured publicly available grocery prices across major delivery platforms, delivering normalized pricing data by ZIP code, platform, and product category.
The brand gained granular visibility into local pricing dynamics, identified regional pricing gaps faster, and made more informed pricing decisions based on real market conditions rather than national averages.
A US proptech team relied on manual tracking across multiple real estate portals, making it difficult to consolidate listing data, monitor market changes, and maintain current datasets for analysis.
A structured daily property listing feed that consolidated listings, pricing, and status updates from major real estate portals into a standardized schema ready for direct integration with the team’s models
The team eliminated manual data collection, gained daily visibility into new listings and market changes, and improved decision-making with clean, current, and consistently structured property data.
A US restaurant brand lacked visibility into menu pricing, delivery fees, and service charges across multiple food delivery platforms, leading to pricing inconsistencies and hidden margin erosion.
A consolidated monitoring solution that tracked menu prices, delivery fees, service charges, and competitor pricing across major delivery platforms, providing a unified daily view of pricing
The brand gained complete cross-platform pricing visibility, identified margin leaks faster, improved pricing consistency, and eliminated the need for manual platform-by-platform monitoring.
A European hotel group lacked visibility into competitor room rates across booking channels, making it difficult to optimize pricing, react to demand shifts, and maximize revenue opportunities.
A daily hotel rate intelligence feed that monitored competitor pricing across key booking channels, properties, and stay dates, delivering normalized market data for revenue management teams.
The group gained real-time market visibility, improved pricing decisions, responded faster to demand fluctuations, and reduced manual competitor rate checks across multiple channels.
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.
Every example above followed this same straightforward path.
We define the problem, target sites and the fields you need.
We build and deliver a validated sample in 3–7 days.
We adjust the schema and coverage until it fits.
The pilot becomes a maintained, monitored pipeline.
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.
Tell us the challenge you're facing and we'll return a sample dataset within 1 business day.