Resume Intelligence Platform with Qdrant + OpenAI RAG
Semantic talent search with explainable JD matching and conversational AI chat over candidate profiles
Business Context
Transforming talent acquisition with AI-powered semantic search and intelligent matching
The Client
TalentHub Pro, a leading recruitment technology platform serving 500+ enterprises across North America and Europe.
Business Goal
- •Replace keyword-based search with semantic understanding of skills and experience
- •Enable JD-to-resume matching with explainable AI recommendations
- •Provide conversational AI chat over candidate profiles with citations
- •Support hybrid search combining semantic + keyword matching
Key Challenges
Traditional recruitment systems struggled with semantic understanding and intelligent matching
Poor Keyword Search
Traditional keyword search missed qualified candidates. Searching "Spring Boot" wouldn't find candidates who wrote "Java microservices with Spring Framework".
60% of relevant candidates were missed by recruiters
No Semantic Understanding
System couldn't understand skill relationships. Redis caching experience wasn't matched with Memcached or distributed caching expertise.
Narrow search results limited talent pool discovery
Manual JD Matching
Recruiters manually read resumes against job descriptions. No scoring, no explainability, no automation at scale.
15-20 minutes per candidate review, unsustainable at scale
Limited Filtering
Couldn't combine semantic search with structured filters like years of experience, location, education level, or role.
Results included irrelevant candidates or missed qualified ones
No Conversational AI
Recruiters had to read entire resumes. Couldn't ask "What's their AWS experience?" or "Have they led teams?"
Slow candidate evaluation, poor recruiter experience
Version Control Issues
When candidates updated resumes, the system couldn't track changes or re-index intelligently. Full re-processing was expensive.
Stale data and high computational costs for updates
Solution Architecture
Modern vector search platform with Qdrant, OpenAI, and RAG for intelligent talent matching
Qdrant Vector Database
Each resume chunked by section (Experience#N, Skills, Projects). Each chunk stored as a Qdrant point with vector embedding + JSON payload (candidate_id, section, dates, title, location, years_experience).
OpenAI Embeddings
Text-embedding-3-large generates 1536-dim vectors for each resume chunk and search queries. Semantic understanding captures skill relationships and context.
Semantic Search + Filters
Recruiters query naturally: "Spring Boot + Redis + AWS + 5+ years + Chicago". Qdrant combines vector similarity with payload filtering for precise results.
JD Matching with RAG
Job descriptions chunked and embedded. For each candidate, retrieve top matching resume chunks, send to LLM for match score, strengths with citations, gaps, and interview questions.
Candidate AI Chat
Recruiters ask questions like "What's their AWS experience?" System retrieves relevant resume chunks restricted to that candidate, LLM answers with citations to specific sections.
Version Control & Sync
MySQL stores canonical resume data. When updated, only changed sections re-embedded and upserted to Qdrant. Point IDs deterministic (candidateId + version + section).
Measurable Results
Transformative impact on recruiter productivity and placement success
Sub-50ms semantic search with Qdrant HNSW index, even across 10M+ resume chunks
JD-to-resume matching accuracy verified against recruiter feedback and placement success
Recruiters find qualified candidates 80% faster with semantic search + AI chat
Semantic understanding surfaces 3x more relevant candidates compared to keyword search
Recruiters rate the AI chat and explainable matching as highly valuable features
Reduced time-to-hire and improved placement success delivering $2.4M annual cost savings
Technical Architecture
Modern microservices with React, Spring Boot, Qdrant, MySQL, and OpenAI
Frontend Layer
Semantic search UI, JD matcher, AI chat interface
API state management and caching
Modern, responsive UI design
Backend Services
REST APIs, business logic, orchestration
Vector search, filtering, upserts
Embeddings + chat completions
Data Layer
System of record: candidates, resumes, versions
10M+ resume chunks with vectors + payload
Search result caching, session storage
Infrastructure
Container orchestration, auto-scaling
Multi-AZ MySQL with automated backups
Monitoring, alerting, performance metrics
Business Impact
AI-powered resume intelligence transforming enterprise talent acquisition
GenAI Expertise Demonstrated
- •Embeddings-first architecture with Qdrant vector database
- •RAG with citations for grounded, explainable AI responses
- •Hybrid search combining semantic + keyword matching
- •Payload filtering + indexes for production-grade queries
Operational Excellence
- •80% faster sourcing reducing time-to-hire from weeks to days
- •3x more relevant results expanding recruiter talent discovery
- •Sub-50ms search providing instant semantic matching
- •$2.4M annual savings from improved placement success
User Experience
- •92% user satisfaction with conversational AI chat feature
- •Explainable matching builds recruiter trust and confidence
- •Natural language queries eliminating boolean search complexity
- •Grounded citations enabling quick resume validation
The Resume Intelligence Platform showcases enterprise-grade GenAI implementation with Qdrant vector database and OpenAI. By combining semantic search, RAG with citations, and hybrid matching, Ensar Solutions delivered a production-ready system that transforms talent acquisition with AI-powered intelligence, explainability, and measurable ROI.
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