Past Projects Map 

The goal was to transform years of completed-project data into something more useful than an internal archive.

The client already had a growing record of completed installations within its field-photo ecosystem, but that information was not easily accessible to prospective customers browsing the website. The new system needed to convert that operational data into an intuitive map where homeowners could explore completed projects throughout the service area, filter by product type, and quickly answer a highly valuable question: “Has this company completed projects like mine near where I live?”

• Transforming field-service data into public-facing map data. Project information originating from the company’s field-photo workflow needed to be extracted, classified, normalized, geocoded, and converted into a structure suitable for browser-based visualization.
• Communicating large project volumes without overwhelming the map. Displaying every completed project independently at regional zoom levels would create an unreadable field of overlapping markers, requiring density-aware clustering that progressively reveals individual projects as users zoom.
• Categorizing inconsistent project labels. Field data can contain numerous product and photo labels, requiring a deterministic classification layer capable of translating those records into clear customer-facing categories such as Windows & Doors, Tubs & Showers, Patio Products, Siding, and Decks.
• Protecting customer privacy. Completed work needed to demonstrate geographic coverage without publicly exposing customer names or unnecessary personally identifiable information.
• Making geography useful rather than merely visual. A traditional map could show where projects exist, but homeowners needed an easy way to understand the work surrounding their own location.
• Connecting engagement to measurable conversion. The map needed to function as more than an informational feature by providing a trackable path from geographic proof-of-work to quote generation.

• Built a Node.js-driven project data pipeline that converts field-project records into normalized map data, allowing the visualization layer to remain independent of the original operational data source.
• Integrated production project data with the CompanyCam workflow, using field-photo metadata and project labels to classify completed installations into standardized customer-facing product categories.
• Developed an interactive Leaflet visualization layer with marker clustering so regional views communicate project density while progressively revealing individual completed projects as users explore specific areas.
• Created dynamic filtering and search functionality that allows users to narrow the dataset by project category, city, state, and other relevant project information without reloading the application.
• Engineered a location-aware “Near Me” experience using browser geolocation and geographic distance calculations to identify completed projects within selectable 5-, 10-, 25-, and 50-mile radii around the visitor.
• Designed privacy-safe project presentation so public markers communicate project type and general location without displaying customer names or other unnecessary personal information.
• Added an attributable conversion path connecting the location experience directly to client’s quote workflow through campaign-tagged calls to action, allowing leads originating from the map to be identified downstream.
• Optimized the application for website embedding and responsive use, including clustered rendering, vector map tiles, fullscreen controls, compact iframe presentation, accessibility considerations, and responsive interfaces for desktop and mobile visitors.

The finished system turns operational project history into an interactive marketing asset. Instead of simply telling prospective customers that the client serves their area, the map allows them to visually explore completed work, understand project density, filter for installations relevant to their needs, and identify comparable projects near their own location.

The architecture also separates project-data processing from presentation, allowing updated project datasets to refresh the visualization without requiring changes to the mapping application itself. What began as a request to display past projects ultimately became a reusable geospatial data platform that combines field-service integration, data transformation, privacy-conscious visualization, social proof, and measurable lead generation.