The Challenge
A German startup was struggling with traditional keyword-based search that failed to understand user intent and context. Their users were frustrated with irrelevant results, and the business was losing opportunities due to poor search performance. They needed a solution that could understand the semantic meaning of queries and return truly relevant results.
Project Overview
Industry
Technology / SaaS
Location
Germany
Timeline
3 months
Status
Our Solution
We developed a sophisticated semantic matching algorithm that combines embedding-based search with LLM-powered reranking. The solution was built as a FastAPI microservice that seamlessly integrates with their existing Laravel platform.
Semantic Understanding
Implemented embedding-based search using state-of-the-art models to understand the semantic meaning of queries beyond simple keywords.
LLM Reranking
Integrated large language models to rerank search results based on relevance and context, dramatically improving result quality.
Seamless Integration
Built as a FastAPI microservice with RESTful endpoints that integrate seamlessly with the existing Laravel platform without disrupting current operations.
Scalable Architecture
Designed for high performance and scalability to handle growing search volumes with minimal latency.
Technologies Used
Results & Impact
85%
Improvement in search relevance
60%
Faster query processing
40%
Increase in user engagement