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.
Product review intelligence across Amazon, Walmart & Target
A US consumer-insights team needed to understand what shoppers actually thought about its products and competitors, but reviews were scattered across retailers, inconsistently structured, and difficult to analyze beyond basic star ratings.
A matched, multi-retailer review dataset capturing full review text, ratings, verified-purchase signals, variants, dates, and timestamps, with products matched across retailers, duplicate reviews removed, and sentiment and themes extracted for analysis.
The team gained scalable sentiment intelligence, uncovered the reasons behind positive and negative ratings, benchmarked competitors consistently, detected emerging demand through review velocity, and maintained a privacy-clean dataset using only public review content.
Automated MAP violation monitoring across marketplaces
A US consumer brand struggled to identify third-party sellers violating its Minimum Advertised Price (MAP) across multiple marketplaces, especially when violations appeared briefly and disappeared before manual checks.
An evidence-grade MAP monitoring solution that matched products to marketplace listings, tracked seller-level offers, compared prices against MAP thresholds, and captured timestamped violation evidence across every seller.
The brand gained continuous visibility into MAP violations, captured enforceable seller-level evidence, identified repeat offenders, and strengthened pricing control, margins, and authorized retailer relationships.
Job posting intelligence for hiring market analysis
A US recruiting-tech startup needed a large-scale, structured dataset of public job postings to analyze hiring demand, in-demand skills, geographic trends, and role difficulty without building its own data-engineering operation.
A normalized job-postings dataset that standardized titles into role families, extracted and normalized skills, de-duplicated postings across career pages and job boards, and tracked posting lifecycles, locations, remote status, and days-open.
The startup gained an analysis-ready labor-demand dataset for role, skill, regional, and hiring-trend intelligence while maintaining a strict privacy boundary using only public job-posting content and no applicant or candidate data.
ZIP-level rental market intelligence across US properties
A US rental-analytics platform needed clean, ZIP-level apartment rents and availability from multiple listing sites, but duplicate listings, imprecise locations, changing rents, and hidden concessions made reliable market analysis difficult.
A de-duplicated rental intelligence dataset capturing property details, unit types, ZIP and neighborhood location, asking rents, concessions, availability, days-on-market, rent changes, and timestamps across major rental platforms.
The platform gained trustworthy ZIP-level rent benchmarks, accurate distinct-unit statistics, seasonal and days-on-market insights, and concession trends that helped reveal local market direction.
Real-time airfare monitoring for price alerts
A US travel app needed fresh, reliable airfare data across airlines and OTAs to power price alerts and “buy now or wait” recommendations without maintaining an in-house fare monitoring operation.
A parameterized fare-monitoring feed capturing routes, travel dates, cabin, passengers, fares, booking sources, lead time, and timestamps, with historical fare data to support price forecasting.
The app launched reliable airfare alerts, enabled best-time-to-book recommendations using historical price curves, compared fares across airlines and OTAs, and scaled monitoring as new routes and users were added.
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.
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.