Project Meridian
Full-Stack AI Commerce Integration — Intelligent Discovery, Content & Service Automation
Challenge
A mid-market e-commerce operator had a functional platform but static discovery experience. Every visitor encountered the same catalogue, keyword-only search, and no mechanism to surface relevant inventory for exploratory intent. The catalogue backlog represented weeks of pending manual copywriting.
AI Solution Applied
Architected a full-stack AI integration layer via API middleware: semantic product search (OpenAI embeddings + Pinecone), AI-generated product descriptions at catalogue scale, collaborative filtering recommendation engine (Python, scikit-learn), and AI-assisted customer support chatbot with human escalation.
Business Outcome
Semantic search drove 30–40% improvement in discovery depth. The catalogue backlog (6+ weeks of manual copywriting) was completed in a single batch. Customer support ticket deflection reached ~35% within 60 days. Conversion rate in AI-touched visitor segment was 15–25% higher than baseline.
Enterprise Relevance
The defining architectural decision: AI capability layered over an existing platform via clean integration boundaries, with no modification to the proven core application. Directly transferable to legacy ERP augmentation, CRM intelligence layers, and supply chain optimisation overlays.