Semantic Search & Discovery for E-commerce
Enable customers to find products effortlessly using AI-driven semantic search that understands intent beyond keywords.
Why Semantic Search Matters
- Reduce zero-result searches and improve customer satisfaction.
- Understand synonyms, context, and user intent through vector embeddings.
- Support image and voice search for a modern shopping experience.
- Hybrid approach combining traditional search with AI-powered relevance.
- Local Leeds expertise with global implementation capabilities.
How We Work: 5-Step Process
Data Preparation
Extract and preprocess product text, metadata, and assets.
Embedding Generation
Create vector embeddings for product content.
Indexing
Store embeddings in a scalable vector database.
Integration
Connect semantic search service with storefront via APIs or middleware.
Evaluation & Optimization
Monitor search performance and continuously refine models.
Core Use Cases
- Keyword fallback: Understand user intent when exact matches fail.
- Voice search integration for mobile shoppers.
- Image-based product discovery using visual embeddings.
- Localization: Multilingual search capabilities for global audiences.
- Personalized search results based on user behavior and preferences.
Success Metrics & ROI Examples
- Zero-Result Reduction: Decrease no-results by up to 40%.
- Search-to-Conversion Rate: Increase conversions from search by 10–15%.
- Engagement: Higher click-through rates on search results.
- Latency: Maintain low response times for search queries.
- User Satisfaction: Improved satisfaction metrics through relevant results.
Why Choose 67commerce & DeepGreenLabs.ai
- Expertise in vector search and embeddings tailored for e-commerce.
- Platform-agnostic integration with scalable vector databases (Pinecone, Elasticsearch, etc.).
- Continuous monitoring and tuning to maintain search relevance.
- Support for advanced search modalities: voice, image, and multilingual.
- Security-first approach: safe handling of product data and user queries.
Frequently Asked Questions
How does semantic search differ from keyword search?
Semantic search uses vector embeddings to understand intent and context, returning relevant results even when exact keywords aren’t matched.
Which vector databases do you use?
We work with Pinecone, Elasticsearch with vector support, and other scalable vector databases based on client needs.
How long does integration take?
A pilot semantic search integration can be completed in 4–6 weeks, including data prep and testing.
Can semantic search handle multiple languages?
Yes, we implement multilingual embeddings to support global audiences on your store.
What data is needed?
Product titles, descriptions, metadata, and optionally image embeddings for visual search.
