AI-Powered Local Service Search: Application of Hybrid Semantic Search in Local Service Discovery

This article introduces a hybrid semantic search engine that combines Claude AI, full-text matching, and geolocation sorting, focusing on helping users quickly find local professionals like plumbers and electricians. Built using the MERN stack, this project aims to address the pain points of traditional local service search—such as insufficient intent understanding and lack of geolocation sorting—and connect service demanders with providers.

In daily life, finding reliable local service professionals (like plumbers and electricians) is often time-consuming and frustrating. Traditional search relies on keyword matching, which struggles to understand users' true intentions and cannot intelligently sort results based on geographic location. With the popularization of AI technology (especially large language models), it has become possible to reimagine the local service search experience.

Hybrid Semantic Search

The system integrates three complementary technologies:

1. Claude AI Semantic Understanding: Uses Anthropic's Claude large language model to capture the true intent behind user queries (e.g., "kitchen pipe leak" is understood as an emergency plumbing service);
2. Full-text Matching: Supplements precise keyword matching, suitable for scenarios where users know specific professional terms;
3. Geolocation Sorting: Prioritizes nearby available service providers to meet emergency service needs.

Technology Stack Selection

The MERN stack (MongoDB, Express.js, React, Node.js) is used:

• MongoDB's flexible document model is suitable for storing diverse service provider information;
• Express.js: A lightweight API framework;
• React: Builds responsive UIs;
• Node.js: Asynchronous features handle high-concurrency requests.

Value for Consumers

• Smarter search experience: Describe problems in natural language to get relevant results;
• Geolocation awareness: Prioritize nearby service providers to reduce waiting time;
• Multi-dimensional matching: Integrate factors like service quality, distance, and availability.

Value for Service Providers

• Precise customer matching: AI understanding connects suitable service providers with customers who have real needs;
• Lower customer acquisition costs: Intelligent recommendations reduce invalid inquiries;
• Improved service efficiency: Geolocation optimizes service scope and reduces commuting time.

This project demonstrates the innovative application of AI technology in traditional industries, and the hybrid architecture (large language model + traditional retrieval) provides a reference for the development direction of search technology. Its success can be extended to other vertical fields:

• Healthcare: Help patients find suitable specialists;
• Education and training: Match learners with educational resources;
• Professional services: Discovery platforms for professionals like lawyers and accountants.

As the digital transformation of the local service economy accelerates, intelligent search solutions like Search-Engine will become key infrastructure connecting supply and demand. Integrating AI semantic understanding with traditional search technology is expected to build a more intelligent and efficient local service ecosystem, making finding reliable services as easy as ordering takeout.