--- title: "E-commerce / Custom Manufacturing case study | Craft Digital" description: "A custom AI-powered operations layer that closed the gaps the e-commerce platform couldn't, supporting a custom cabinetry business built on high-value orde" lang: en json-ld: | [ { "@context": "https://schema.org", "@type": "Organization", "name": "Craft Digital", "url": "https://craft-digital-systems.lovable.app/", "logo": "https://craft-digital-systems.lovable.app/og-image.png", "description": "Custom software, AI, automation, and operational infrastructure for mid-market operators and their investors." }, { "@context": "https://schema.org", "@type": "WebSite", "name": "Craft Digital", "url": "https://craft-digital-systems.lovable.app/" }, [ { "@context": "https://schema.org", "@type": "Service", "name": "E-commerce / Custom Manufacturing operational infrastructure", "description": "A custom AI-powered operations layer that closed the gaps the e-commerce platform couldn't, supporting a custom cabinetry business built on high-value orders.", "serviceType": "Internal ops, Inbound conversion, Configurators, CRM, Portals", "areaServed": "United States", "url": "https://craft-digital-systems.lovable.app/case-study/custom-manufacturing-ecommerce", "provider": { "@type": "Organization", "name": "Craft Digital", "url": "https://craft-digital-systems.lovable.app/" } }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Case Studies", "item": "https://craft-digital-systems.lovable.app/work" }, { "@type": "ListItem", "position": 2, "name": "E-commerce / Custom Manufacturing case study", "item": "https://craft-digital-systems.lovable.app/case-study/custom-manufacturing-ecommerce" } ] } ] ] --- [![Craft Digital](/__l5e/assets-v1/bc7a6fac-7a94-4caa-a751-a7e463885a50/craft-logo-full-color.svg) ](/) [Solutions](/solutions)[Custom Development](/custom-development)[Case Studies](/work)[About](/about) Book a Call Menu [← Back to case studies](/work) E-commerce / Custom Manufacturing # Every shipping quote took half an hour. Dozens of times a day. A custom AI-powered operations layer that closed the gaps the e-commerce platform couldn't, supporting a custom cabinetry business built on high-value orders. Custom React Magento APIs Zoho CRM AI/OCR Pallet packing algorithm Retell Broken Half an hour to build one shipping quote — dozens of times a day, often wrong. Built An operations layer over the store and the CRM: instant freight, one customer screen, OCR on invoices. Now 25 minutes returned per quote, and 18+ inbound routes resolve without escalation. 01 // Before and after shipping quote · generated Elapsed · 00:00:41 Pallets 2 · 48×40×62 Freight class 125 Estimate $684.00 Ready to send ✓ shipping quote · manual Elapsed · 00:24:18 · Measure each carton by hand · Guess pallet configuration · Look up freight class · Call the carrier · Re-quote — dimensions were off Before · 25 minutes After · Seconds ‹› The same quote, two different days of work. 02 // The challenge ## The _challenge._ Bridge the gaps the platform left open — accuracy, speed, and customer service capacity on high-value custom orders. - 01 Shipping quotes for bulky custom orders took 20–30 minutes each and were often inaccurate - 02 The platform couldn't swap colors on a configured build without manually re-adding every SKU - 03 Reps had to dig across disconnected CRM objects to get a full picture of one customer - 04 Manual invoice review before manufacturer handoff was error-prone and expensive when missed Why off-the-shelf wouldn't work This is a high-AOV custom-build business with a customer service team running parallel to a designer team, on top of an e-commerce platform and a CRM that were never built to meet in the middle. No off-the-shelf tool sits in that gap. 03 // What we built 01 ### Instant shipping quotes Carton dimensions go in, a pallet-packing algorithm works out the configuration, and a freight estimate comes back before the customer has finished the sentence. freight estimator Order | Pallets 2 · 48×40×62 Weight 1,240 lb Freight class 125 Estimate $684.00 Quoted ✓ 02 ### Cart automation A finish change used to mean removing and re-adding every SKU on a configured build. Now one swap remaps the whole cart without touching a line item. cart · color swap Finish Natural Oak → Slate BASE-24-L queued WALL-30-U queued TALL-84-P queued TRIM-96-S queued PANEL-END-R queued Cart rebuilt · 0 re-entry ✓ 03 ### One-screen customer view A phone number, email, or order ID assembles orders, chat history, designer notes, and invoices into a single panel, pulled in parallel from systems that never spoke. customer view · one screen (555) 014-2280 Orders 3 open · 11 lifetime Designer notes Slate finish approved 04/12 Chats 2 threads · last 6 days ago Invoices 1 pending manufacturer PO One screen replaced six tabs and a guess. 04 Invoice OCR Incoming invoices compared to manufacturer purchase orders, discrepancies flagged before handoff. 05 18-route intake system Assembly help, designer scheduling, returns and damages, sales — handled without escalation. 06 After-hours chat First-layer triage covering the hours the team is offline. Live · production ## Inbound resolving _without escalation._ intake routes · live Live 19:42 Assembly help · route 06 Resolved 20:04 Designer scheduling · route 02 Booked 20:19 Damage claim · route 11 Logged 20:33 Shipping estimate · route 04 Quoted 20:47 Order status · route 01 Resolved 04 // Impact Annualized impact 0 Minutes returned per shipping quote, dozens of times a day Time to build a shipping estimate Manual, frequently inaccurate 25min Instant, generated by the system 1min Repeated dozens of times daily 05 // Technical implementation ## Architecture _overview._ A custom React tooling layer wrapping the e-commerce platform and CRM through their APIs, with parallel API orchestration behind the unified customer view and AI/OCR behind invoice review. A pallet-packing algorithm produces freight estimates in seconds. The intake system and chat run on the same data layer, so customer-facing and internal AI share one source of truth. technical-highlights.md - → Pallet packing algorithm for instant freight estimation - → Platform API orchestration for live cart manipulation - → Parallel API requests across CRM objects for the unified view - → OCR-based invoice comparison against purchase orders - → Intake system with 18+ conversational routes - → After-hours chat triage into the same records 06 // Field notes The platform wasn't wrong. It just stopped where the business kept going. One screen replaced six tabs and a guess. 07 // Related case studies [ Manufacturing / Enterprise ### The Manufacturer's Operating System. Seven connected systems on one data foundation, delivered in sequence — each live before the next begins. Read case study ](/case-study/enterprise-manufacturer-operating-system)[ Automotive Services ### The whole business ran on spreadsheets. Six locations, each with its own spreadsheet and a tab for every day — replaced by one scheduling system, with an intake system covering the phones around the clock. Read case study ](/case-study/multi-location-automotive)[ Field Services / Multi-Location Franchise ### The phone rings at 7pm. Nobody's there. Nights, weekends, and storm-season surges — an AI voice system that answers, qualifies, and books the appointments the business used to sleep through. Read case study ](/case-study/tree-care-after-hours) ## Let's talk about _what you're trying to fix._ Thirty minutes. No pitch deck. Book a conversation [![Craft Digital](/__l5e/assets-v1/b959f9c9-7914-488d-b4ec-368b171a61e0/craft-logo-white.svg) ](/) We build AI software that replaces spreadsheets, disconnected systems, and manual work. 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