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 electronics brand struggled to monitor unauthorized discounting across global marketplaces, allowing rogue sellers to violate MAP policies, trigger price wars, and damage distributor trust.
An automated MAP monitoring solution tracking pricing, seller activity, stock availability, and Buy Box ownership across 20+ marketplaces with hourly data extraction, real-time alerts, and compliance evidence
The brand achieved an 85% reduction in MAP violations, improved channel partner confidence, accelerated enforcement actions, and protected over $1.5M in distribution margins.
A leading grocery chain lacked visibility into localized competitor pricing, with geo-fenced catalogs, dynamic regional pricing, and anti-bot protections preventing accurate market benchmarking across thousands of ZIP codes.
A large-scale price intelligence platform that monitored 15M+ daily SKUs across 2,500+ US ZIP codes using geo-targeted data extraction, residential proxy infrastructure, automated parsing, and normalized
The retailer gained granular local market visibility, achieved 99.95% data pipeline uptime, optimized dynamic pricing decisions, and responded faster to competitor promotions while reducing internal data engineering costs.
A US marketplace seller was losing the Buy Box whenever competitors changed pricing, but the team had no clear visibility into which sellers or price movements were causing the loss.
A daily Buy Box and competitor price monitoring pipeline tracking seller activity, pricing changes, and
The seller gained real-time visibility into competitor pricing behavior and Buy Box shifts, allowing the team to react faster, optimize pricing decisions, and reduce Buy Box losses.
A US retailer’s pricing team relied on slow and partial manual competitor checks across multiple retail websites, making pricing decisions outdated by the time they were finalized.
A daily competitor price intelligence feed covering key products across multiple retailers, delivered into the retailer’s pricing workflow in a clean and consistent data structure.
The retailer replaced manual price tracking with a reliable automated daily feed, helping analysts save time and respond faster to competitor pricing changes with more accurate market visibility.
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.