Semantic Search & Match | Vector Search for Accurate Results

Semantic Search & Match Engine

Semantic Search & Match (Node.js + Python)

We implemented a semantic search and matching engine that understands user intent instead of relying on exact keywords. The system powers fast, accurate discovery across large catalogs where titles, descriptions, and profiles are constantly changing.

Using vector embeddings, hybrid retrieval, and a lightweight RAG layer, the engine delivers results that feel intelligent and human-aware: similar concepts are grouped together, weak keyword matches are filtered out, and each suggestion can be backed by a concise explanation.

Sentence embeddings Vector search + BM25 RAG explanations Relevance tuning

Impact at a glance

+28%

Click-through rate

−62%

Zero-result queries

ms

Sub-second responses

24/7

Production uptime

These improvements came from better intent understanding, smarter ranking, and continuous monitoring of how users interact with search results in production.

Problem

The existing keyword search struggled with:

Missed matches when users used different wording or synonyms.
Irrelevant results ranking at the top for generic queries.
A high number of zero-result pages on long-tail searches.

Solution

We introduced an AI-powered semantic layer:

Transformer-based embeddings for jobs, profiles, and rich content.
Vector search combined with BM25 for robust hybrid retrieval.
RAG for short, human-readable explanations of each match.

Outcome

The new engine significantly improved discovery:

Users find relevant results faster, even with vague queries.
Search feels “smarter” and closer to how humans think.
The business gets more engagement and higher conversion from search traffic.

Architecture overview

The semantic engine plugs into the existing stack as a dedicated AI service. It exposes a clean API for the web application while handling all the heavy lifting of embeddings, vector search, and ranking behind the scenes.

Node.js API gateway receives search and match requests, performs validation, authentication, and logging.
Python ML service generates or updates embeddings, and talks to the vector store for nearest-neighbor lookups.
Hybrid retrieval combines vector results with BM25 keyword hits for robustness on noisy or short queries.
Reranking layer re-orders candidates using domain-specific signals like clicks, applies, saves, and business rules.
RAG explanation module fetches contextual snippets and generates a one-line reason for each top result.

Key features in production

Intent-aware search

Users can search in natural language (“remote senior backend role in fintech”) and still get accurate, ranked matches.

Real-time matching

New jobs or profiles become searchable quickly via incremental embedding and indexing workflows.

Explainable results

Each suggestion comes with a short reason (“matches your skills in X, Y and recent experience in Z”), improving trust.

Monitoring & A/B testing

Dashboards track CTR, zero-result rates, latency and allow controlled experiments on new ranking ideas.

The entire flow is designed to be extendable: new models, new ranking rules, or new data sources can be plugged in without breaking the public API.

AI & ML capabilities

Semantic search with transformer-based sentence embeddings.
Vector databases and ANN (approximate nearest neighbor) search.
Hybrid retrieval (dense vectors + BM25 keyword scoring).
Retrieval-Augmented Generation (RAG) for short explanations.
Custom similarity functions and task-specific scoring.

Engineering & MLOps

Node.js REST APIs for search, match, and recommendations.
Python services for model inference and evaluation pipelines.
Batch and streaming embedding updates for fresh data.
Logging and metrics for latency, throughput and quality.
Versioning and safe rollout of new models and ranking logic.

Typical use cases

Matching candidates to jobs or projects based on skills and experience.
Semantic search across large knowledge bases and help centers.
Product discovery for big catalogs where simple filters are not enough.
AI copilots that need grounded, explainable answers from internal data.
Any scenario where “find similar items” or “recommend next best option” is critical.