8nap- Semantic Search Engine

The solution combines vector-based semantic search with lexical and hybrid methods to interpret user queries more naturally, enabling discovery even when exact keywords don’t match product catalogs. Fine-tuned models on customer-specific data ensure domain relevance, delivering accurate results for complex, long-tail, and intent-driven queries. Powers multi-modal and conversational search experiences, boosting user engagement and conversion rates by surfacing the most relevant results, faster. Scalable across AWS cloud with flexible deployment options

Features:

• Lexical, Semantic, and Hybrid Search: Supports exact match and contextual understanding with a combined query strategy.

• Intent-Aware Search Engine: Detects user search goals to enhance relevance and personalization.

• Custom-Trained Transformers: Domain-specific fine-tuning for superior accuracy in product and content discovery.

• Multi-Modal Search: Processes diverse inputs including text, metadata, and images.

• Conversational Product Advisor: Interactive interface offering real-time product recommendations and query refinement.

• High-Performance at Scale: Designed for concurrent, low-latency search across large catalogs.

• Feedback Loops with Active Learning: Continuously learns from user interactions to improve relevance over time.

Key Implementations

1. Model Deployment on EC2 GPU Clusters: Train and serve custom search and recommendation models at scale.

2. Search Index Management with Amazon OpenSearch: Real-time indexing and querying for hybrid and semantic search.

3. Personalization using EFS and S3 for Feedback Logs: Store and retrieve user behavior data for model updates.

4. Model training and retraining pipeline automation using Lambda and Bedrock with scalable serverless logic