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.
Automated marketplace repricing with live competitor data
An ecommerce seller needed reliable competitor pricing across Amazon, Walmart, and SHEIN to automate repricing while avoiding inaccurate matches, stale data, and margin-eroding price wars.
A live marketplace intelligence feed delivering SKU-matched competitor pricing, Buy Box/featured offer data, landed prices, availability, and freshness metadata through a normalized API for automated repricing.
The seller deployed a margin-safe repricing engine with accurate competitor intelligence, improved pricing decisions, protected profitability through automated guardrails, and eliminated the burden of maintaining marketplace scrapers.
Competitor hotel rate monitoring across OTAs
A small online travel agency needed visibility into competitor hotel rates across multiple countries, booking channels, weekends, and room types to maintain competitive pricing and monitor rate parity.
A parameterized hotel rate intelligence solution that tracked competitor pricing by property, stay date, room type, rate plan, and booking channel, delivering normalized, timestamped rate data for accurate comparisons.
The OTA gained reliable competitor rate visibility, improved rate parity monitoring, optimized pricing decisions across key markets, and eliminated manual competitor tracking with a scalable monitoring solution.
Restaurant demand intelligence from reservation availability
A university research team needed a consistent restaurant demand dataset across New York City to analyze dining trends and measure the impact of external events using publicly available reservation signals.
A research-ready restaurant-day panel capturing reservation availability, ratings, review counts, and standardized observation data across restaurants, dates, and time slots, structured for causal analysis and academic research.
The team received a reproducible, privacy-safe dataset with complete event-window coverage, enabling robust difference-in-differences analysis, reliable demand modeling, and publication-ready research.
Store-level grocery price comparison across multiple retail chains
A US grocery price-comparison app needed accurate, store-level pricing across multiple retail chains to help shoppers identify the cheapest basket in their local area while ensuring reliable cross-chain product matching.
A normalized grocery pricing feed that matched identical products across retailers, captured store/ZIP-level prices, promotions, availability, and freshness data, and delivered comparison-ready insights through a unified API.
The app launched with trusted local pricing, enabled accurate basket comparisons, reduced engineering effort by eliminating scraper maintenance, and scaled seamlessly into new markets with consistent, high-quality data.
How a US Food-Tech Startup Validated Its Concept with a Grocery Data Sample
An early-stage US food-tech startup needed to verify that grocery product, pricing, and availability data was accurate, complete, and structured correctly before investing in a production-scale data solution.
A production-accurate validation dataset with grocery product information, pricing, availability, categories, cross-source product matching, and freshness metadata in JSON and CSV formats for seamless testing.
The founder validated data quality with confidence, confirmed product-market fit using real production data, reduced infrastructure risk, and established a scalable path from pilot to full production deployment.
Competitor store location intelligence across retail chains
A US retailer lacked a unified view of competitor store locations across chains like ShopRite, Smart & Final, and Meijer, making footprint analysis, site selection, and market expansion planning difficult.
A normalized, geocoded store location intelligence dataset combining multiple retail chains with validated coordinates, store attributes, operational status, and change tracking for GIS-ready analysis.
The retailer gained accurate competitor footprint visibility, streamlined trade-area and site-selection analysis, eliminated manual data collection, and monitored store openings and closures with confidence.
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.