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Development of AI-Powered Semantic Search Bot for Enhanced eCommerce Product Discovery
  1. case
  2. Development of AI-Powered Semantic Search Bot for Enhanced eCommerce Product Discovery

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Development of AI-Powered Semantic Search Bot for Enhanced eCommerce Product Discovery

spaceotechnologies.com
eCommerce
Retail

Challenges with Ineffective Site Search Functionality

The client's existing search system produced irrelevant results, leading to 23% revenue loss through abandoned carts and high bounce rates. Customers struggled to find products due to rigid keyword matching, poor intent recognition, and outdated inventory data.

About the Client

Large-scale B2B/B2C eCommerce platform managing 47,000+ products with search-driven revenue model

Goals for AI-Driven Search Solution Implementation

  • Reduce zero-result search queries by 100%
  • Improve product discovery speed by 23%
  • Increase onsite conversion rate through relevant results
  • Decrease bounce rate by enhancing search experience
  • Automate inventory-aware product recommendations

Core System Functionalities

  • Semantic search with synonym recognition
  • Natural language intent interpretation
  • Realtime inventory-integrated results
  • Misspelling tolerance and correction
  • Cross-platform compatibility with Shopify/Magento/WooCommerce

Technology Stack Requirements

OpenAI GPT-3.5/4 models
Python/NLTK for NLP processing
Node.js backend framework
TensorFlow/PyTorch for ML models
Vector databases for embeddings

System Integration Requirements

  • Shopify API
  • Magento 2 integration
  • WooCommerce REST API
  • ERP/CRM systems for pricing data
  • Realtime inventory management systems

Operational Requirements

  • Support 10,000+ concurrent users
  • Response time under 500ms
  • 99.9% system availability
  • GDPR-compliant data processing
  • Auto-scaling architecture for product catalogs

Expected Business Impact of AI Search Implementation

Projected 23% revenue increase through improved search conversions, 40% reduction in support tickets related to search issues, and $500K+ annual savings from automated product discovery optimization. Implementation should also enable personalized pricing visibility and reduce cart abandonment rates by 15-20%.

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