Transform Your Search with AI-Powered Semantic Understanding

The Challenge

Traditional keyword-based search is no longer sufficient for modern applications. Users expect intelligent, context-aware search that understands intent, handles natural language queries, and delivers relevant results even when exact matches don't exist. Legacy search implementations struggle with synonyms, typos, semantic understanding, and complex query patterns.

Meanwhile, AI and machine learning have revolutionized what's possible with search. Semantic search, vector embeddings, hybrid ranking, and LLM-powered query understanding can dramatically improve search quality. However, implementing these technologies requires deep expertise in both search engineering and modern AI techniques—expertise that most organizations lack internally.

Semantic Search
in Action

We've helped organizations across industries transform their search experience with semantic understanding. Here are some of the use cases we've delivered.

eCommerce Product Search

Helped a major online retailer move from keyword matching to semantic product discovery. Shoppers searching for “summer dress for beach wedding” now find relevant results even when product titles don't contain those exact words. The upgrade drove a 35% increase in search-to-purchase conversion and a significant drop in zero-result searches.

Real Estate Listings

Built a hybrid search platform for a real-estate directory serving millions of listings. Natural language queries like “quiet family neighborhood near good schools” now surface relevant properties by understanding intent rather than requiring exact filter selections—improving user engagement and time-to-lead.

RAG & AI Chatbots

Designed and implemented retrieval-augmented generation pipelines that ground AI chatbot responses in proprietary enterprise data. By combining hybrid search with LLM-powered answer generation, we reduced hallucinations and delivered a chatbot that cut research time by 79% for end users.

Knowledge Operations

Replaced a basic keyword search with a hybrid retrieval framework for a knowledge-operations platform serving thousands of articles. By combining question generation, semantic embeddings, and hybrid search, queries now return 1–3 highly relevant matches instead of dozens—and the system highlights knowledge gaps to drive smarter content creation.

Security Intelligence

Built semantic search capabilities over a large corpus of security reports and geopolitical intelligence. Analysts now query the archive in natural language and receive precise, source-cited answers with supporting visualizations—saving analysts 7 hours of manual research per week.

Media & Content Discovery

Implemented semantic search across a large media library combining text, metadata, and user-generated content. Searches like “uplifting documentary about ocean conservation” now return relevant titles by understanding topic and sentiment, driving higher content engagement and longer session times.

Semantic Search
Capabilities

Vector & Semantic Search

Move beyond exact keyword matching to understanding meaning and context. Vector embeddings powered by modern language models enable search results based on semantic similarity, dramatically improving relevance for natural language queries and finding results even when exact terms don't match.

Hybrid Search

Combine the precision of keyword search with the intelligence of semantic search. Hybrid search blends traditional BM25 ranking with vector similarity, delivering optimal results across diverse query types. Fine-tuned relevance weighting ensures the best of both approaches for your specific use case.

LLM Query Understanding

Leverage large language models to understand user intent, expand queries with synonyms and related terms, correct spelling errors, and optimize complex queries for better performance. LLM-powered query parsing can extract structured filters from natural language and handle conversational search patterns.

Advanced Relevance Tuning

Optimize search ranking with sophisticated relevance tuning strategies. Implement learning-to-rank models, personalized ranking, business rules, and click-through analytics to continuously improve search quality and align results with business objectives and user preferences.

Agentic RAG

Build intelligent Retrieval-Augmented Generation applications where AI agents can search, reason, and synthesize information from your data. Enable conversational interfaces, automated research assistants, and intelligent document analysis that goes far beyond traditional search capabilities.

Production Vector Search at Scale

Implement production-ready vector search capable of handling billions of embeddings with millisecond latency. Optimize HNSW indexes, manage embedding generation pipelines, and architect distributed systems that scale vector search to enterprise requirements without sacrificing performance.

Why Choose BigData Boutique

Deep Search & AI Expertise

Our team combines 13+ years of search engineering experience with cutting-edge AI/ML expertise. Led by OpenSearch Ambassador Itamar Syn-Hershko, we understand both traditional search techniques and modern AI approaches, enabling us to architect optimal hybrid solutions.

Proven Results Across Industries

We've successfully implemented semantic search for e-commerce platforms, real-estate directories, knowledge-base software, and enterprise applications. Our track record includes measurable improvements in relevance, conversion, and user satisfaction.

Production-Ready at Scale

We don't just build prototypes—we deliver production-grade implementations with proper monitoring, scaling, security, and reliability. Our solutions are battle-tested at scale and designed for long-term maintainability and evolution.