- 85% reduction in product substitution decision time
- 3x faster staff onboarding with AI-assisted workflows
- 100% consistent output quality across all recommendations
- Custom AI system processing 10,000+ SKUs with visual and attribute analysis
Industry
Product Distribution & E-Commerce
The Challenge: Scaling Intelligence Without Scaling Headcount
Product substitution decisions required expert judgment that was difficult to replicate. Customer service queues were bottlenecked by a small number of specialists, and new hires took 6+ months to ramp up to full productivity. Traditional rule-based automation failed to handle the complex, context-dependent nature of these decisions.
The client needed a system that could capture years of specialist knowledge and make it available to every team member, instantly.
The Solution: AI Recommendation Engine
AspireVita designed a two-phase engagement combining strategic roadmap development with tactical implementation of a custom AI system.
The platform delivers visual and attribute similarity analysis across 10,000+ SKUs, context-aware substitution suggestions that account for customer preferences and inventory, a continuous learning loop that improves with specialist feedback, and integration with the client's existing ERP and CRM systems.
AspireBlueprint
AspireVita's strategic AI roadmap framework guided the phased rollout of this recommendation engine.
Explore AspireBlueprintUnder the Hood
The system combines Computer Vision models for visual product matching, NLP and attribute extraction for understanding product specifications, vector embeddings for semantic similarity search, and RAG (Retrieval Augmented Generation) for generating contextual recommendations grounded in real product data.
Business Impact
The results transformed the client's operations. Decision time dropped by 85%, meaning customers received accurate substitution suggestions in seconds rather than hours. Staff onboarding accelerated 3x because new team members could rely on the AI system from day one. Output quality became 100% consistent, eliminating the variability that came with different specialists handling different queries.