Concept project demonstrating how Tedmark modernizes Sun & Stars's digital presence with AI-powered quotation and lead generation, enhancing online visibility and customer engagement.
Sun & Stars Engineering Ltd. is a hypothetical Ghanaian steel fabrication and engineering company with over 15 years of experience. Despite delivering high-quality structural steel works, warehouse construction, and heavy engineering projects, the company relied heavily on referrals and phone calls for sales, lacking a modern digital presence. Tedmark designed a comprehensive digital transformation solution including a responsive corporate website, an AI quotation assistant that processes project drawings and generates estimates, a 24/7 AI customer assistant, SEO optimization, and a lead management dashboard. The solution integrates Next.js, React, Node.js, PostgreSQL, and OpenAI GPT with RAG, along with WhatsApp and CRM integrations. The expected business impact includes increased online enquiries, faster response times, improved lead management, enhanced professional image, and support for regional and international expansion. The 8-week delivery timeline covers discovery, design, development, AI integration, testing, and deployment.
| Challenge | Business Impact |
|---|---|
| Outdated corporate website | Poor first impression and user experience |
| Poor online visibility | Low search engine ranking and missed opportunities |
| No online quotation system | Manual, slow, and inefficient lead qualification |
| Heavy reliance on referrals | Limited and unpredictable lead pipeline |
| No CRM for tracking contacts | Disorganized follow-up and lost potential clients |
AI assistant guides visitors to the right service and provides instant quotes.
Automated quotation generation from uploaded drawings reduces manual effort.
Dashboard tracks enquiries, quotes, conversions, and AI conversations.
Technical SEO, local SEO, and schema markup improve Google rankings.
Responsive design ensures seamless access on all devices.
RAG knowledge base enables accurate, context-aware responses about services and pricing.
The user interacts with the Next.js frontend; requests are handled by the Node.js backend which queries PostgreSQL for data, calls OpenAI GPT with RAG for intelligent responses, and triggers integrations like WhatsApp and email notifications. The lead dashboard aggregates data for management.
We chose Next.js for its server-side rendering and SEO benefits, Node.js for rapid API development, PostgreSQL for relational data integrity, and OpenAI GPT with RAG for accurate, context-aware AI without retraining. This stack allows quick iteration, scalability, and cost-effective deployment for a mid-sized industrial company.
Phase 2 could introduce AI-powered project cost estimation linking material prices and labor rates, a client portal for tracking ongoing orders, and integration with accounting software for invoicing. Phase 3 might include a mobile app for field sales and AR tools for virtual project walkthroughs, further solidifying Sun & Stars's digital leadership.
We chose an AI-first approach because the core challenge was lead qualification—not just visibility. An automated quotation system directly addresses the bottleneck of manual quoting. We considered building a simple form-based quote system but rejected it because it would still require human intervention. The AI assistant with RAG ensures accuracy and scalability. Trade-offs included higher initial AI integration cost, but the long-term gain in 24/7 availability and reduced response time justified it. This strategy aligns with Sun & Stars's goal of expanding beyond Ghana and into international markets where digital presence is table stakes.
What This Project Demonstrates: Tedmark's ability to blend modern web development with AI automation to create a complete digital sales ecosystem for industrial businesses. It shows how even a traditional steel fabrication company can leapfrog competitors by adopting intelligent lead generation and customer engagement tools, transforming their business model from referral-dependent to digitally driven.
