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 regional US grocery chain needed a rich, accurate product catalog for its ecommerce launch, requiring product images, descriptions, ingredients, nutrition facts, and dietary attributes across thousands of SKUs.
A product catalog data enrichment solution that captured images, descriptions, ingredient lists, Nutrition/Supplement/Drug Facts panels, dietary attributes, and optional competitor pricing in a standardized format.
The retailer launched a complete, searchable product catalog, improved customer experience with consistent product content, enabled dietary filtering, and created a scalable foundation for future pricing intelligence.
A public-health research team needed accurate grocery store location data across multiple US retail chains to identify food deserts and analyze healthy food accessibility using GIS-based mapping.
A normalized, geocoded store location dataset combining Kroger and other major grocery chains, including store names, addresses, coordinates, store attributes, and GIS-ready data for seamless spatial analysis.
The research team eliminated manual data collection, accelerated food-access studies, improved mapping accuracy, and gained a reliable multi-chain dataset for reproducible geospatial analysis.
A US AI meal-planning startup needed accurate, store-level grocery pricing across major retailers to generate personalized meal plans and shopping lists based on users' preferred stores and budgets.
A daily grocery price intelligence API delivering normalized pricing, promotions, store/ZIP-level data, and product information across Walmart, Target, Kroger, Amazon Fresh, and Instacart for seamless AI integration.
The platform enabled cost-aware meal recommendations, improved shopping list accuracy, accelerated product development by eliminating scraper maintenance, and scaled effortlessly as new retailers and regions were added.
A US tire retailer needed reliable SKU-level competitor pricing across 15 markets to support pricing and margin decisions, while replacing an existing vendor without disrupting weekly reporting.
A competitor price intelligence solution that matched internal SKUs with competitor listings, delivering weekly pricing, promotional offers, availability, and market-level insights through a standardized data feed.
The retailer achieved seamless vendor migration, maintained uninterrupted weekly pricing intelligence, improved feed reliability, and strengthened pricing decisions with accurate SKU-level competitor data.
A US savings platform needed accurate, real-time grocery prices, coupons, and promotions across 50+ retail chains to deliver reliable price comparisons and shopping recommendations.
A real-time grocery price monitoring solution with freshness-aware data feeds, automated coupon collection, transparent pricing timestamps, and fast API delivery across major US grocery retailers.
The platform delivered up-to-date grocery pricing with defined freshness SLAs, expanded coverage beyond retailer APIs, improved user trust through transparent data freshness, and enabled automated coupon savings.
A US grocery startup needed accurate store-level pricing across 50 grocery chains and 28,000 stores but lacked the resources to build and maintain a large-scale web scraping infrastructure.
A managed grocery price data feed delivering normalized product pricing, promotions, store-level identifiers, and weekly updates through a unified JSON API with CSV export across 50 major US grocery retailers.
The startup launched with reliable grocery pricing data, reduced infrastructure costs by an estimated 60–75%, accelerated time to market, and powered accurate basket price comparisons across thousands of stores.
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.