Emir Aydın
Emir AydınAI AUTOMATION
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LiveLead Software Engineer (Dentech Medikal)

DentechPro

B2B Dental Product Search, Sales, Admin & WhatsApp Order Platform

Next.jsTypeScriptTailwind CSS v4shadcn/uiSupabaseVercel

Problem

B2B medical & dental product distribution required a streamlined platform for clinics to search products, send WhatsApp order requests, and allow admins to manage inventory without complex checkout barriers.

Solution

Engineered DentechPro, a production-ready Next.js MVP for DENTech Medikal. Built with App Router, TypeScript, Supabase, and Tailwind CSS v4, enabling 1-click WhatsApp order generation, product catalog management, and admin dashboards.

System Architecture

Next.js App Router architecture integrated with Supabase client for product catalog indexing and real-time order request triggers via WhatsApp API webhooks.

System Architecture Flow

3 Components
STEP 01
Product Search & Catalog

Fast responsive product search UI with filter categories.

STEP 02
Order Bridge (WhatsApp)

Formatted cart payload generator for direct sales routing.

STEP 03
Supabase Backend

Product database, admin auth, and inventory management.

Execution Workflow

1. Clinic searches dental products on DentechPro catalog.
2. Selected items formatted into structured order request payload.
3. Direct WhatsApp dispatch triggers sales rep notification.
4. Admin dashboard logs request and tracks product availability in Supabase.

Business Impact & Metrics

Provided DENTech Medikal with a high-performance modern B2B platform deployed on Vercel, directly driving clinic product inquiry volume.

Order Request Lead Speed< 5 Secs
Before: Manual PDF quotesAfter: < 5 seconds
Mobile UX Score99/100
Before: Legacy siteAfter: Next.js App Router

Key Lessons Learned

Designing for user preference (WhatsApp orders over complex payment gateways) creates friction-free B2B sales pipelines.

What I Would Build Differently Today

I built DentechPro with clean modular boundaries; today I would add vector search (PgVector) inside Supabase for natural language product query matching.