I built this before applying. Below is a guided product finder for Carbon Designs, a case study explaining the business logic, and a roadmap showing how the same pattern extends across all four of your brands.
High-ticket furniture customers don't browse — they research. This tool replaces decision fatigue with a three-question flow that surfaces the right piece for the right space, written in the voice Carbon Designs already uses.
Standard Shopify collection grid. 40+ products, no guidance. The customer scrolls, compares, and either decides alone or leaves.
Three questions narrow the catalog to 3 matched results. Each result is written to close, not just describe. The grid stays intact below it as a fallback.
Guided selling is a proven conversion lever for high-ticket, high-consideration purchases. West Elm, Article, and RH all use some version of this pattern. The difference here is the copy — each recommendation is written in Carbon Designs' voice, not generic ecommerce language.
Converted to a native Shopify section using Liquid — product data and images pull live from the storefront, no manual catalog maintenance
Embedded at the top of the Carbon Designs collection page as an optional entry point alongside the existing grid
A/B tested as a standalone landing page for paid traffic to measure click-through and add-to-cart lift against the standard collection page
Answer data piped into Klaviyo — someone who selected "outdoor + bold + statement" gets a different follow-up sequence than someone who selected "office + minimal"
This is the actual finder, running in-page. Products link directly to bentonlane.com. Images pull from the Benton Lane CDN.
Three questions. Your matched piece.
All product links go directly to bentonlane.com
The Carbon Designs finder is one instance of a reusable guided selling component. Built right in Liquid, the same system rolls out across every Benton Lane brand — each with its own catalog logic and voice, but sharing the same underlying architecture.
One reusable Liquid section with brand-specific config passed as section settings — built once, deployed four times
Answer data piped to Klaviyo for brand-specific segmentation and follow-up flows per brand
A/B test framework built once, deployed per brand as a standalone landing page for paid traffic
Shared analytics schema so conversion data is comparable across brands from day one
The Carbon Designs version took one day to build as a standalone proof of concept. Converted to a proper Shopify section with shared config, each subsequent brand is faster to deploy than the last. By brand four it's mostly a catalog mapping exercise.
Which brand has the highest drop-off on collection pages, and what does the current Klaviyo segmentation look like across brands. Those two answers would tell me exactly where to focus first.
I built and scaled Earthline Customs from a manual operation to $730K in revenue on Shopify — owning the full order lifecycle, building custom Liquid themes and API integrations, and automating production workflows across DTF, UV DTF, vinyl, and large-format print. That included a full-stack file upload integration with HMAC-verified webhooks, Shopify draft orders API, and Cloudflare R2 storage deployed live in production. Before that I managed Walmart and Sam's Club accounts at Fortis Solutions Group, running daily operations and serving as the direct client liaison for two of their largest retail accounts.
I work through AI tools to build things faster than I could alone — the finder above, a Shopify-to-production pipeline with fuzzy matching, a Google Apps Script with Gemini AI for production auditing. The stack changes but the pattern is the same: identify the problem, build the tool, ship it.
Rogers, AR — open to Bentonville and remote.