Case Study
Svod RAG HR Copilot
Natural language resume search for HR professionals
Built a RAG-powered resume search system that lets HR professionals find candidates using natural language queries, with source attribution and instant results.
Implementation timeline
Requirements & design
Days 1-2Clarified resume parsing needs, search intent patterns, and ChromaDB integration requirements with the client.
Core development
Days 3-7Built FastAPI backend with LangChain RAG pipeline, resume ingestion, and natural language query interface.
Polish & deployment
Days 8-10Added source attribution, refined search relevance, deployed to Render with public demo access.
introduction
Introduction & Background
HR professionals spend countless hours sifting through resumes, often struggling to find candidates that match specific, nuanced requirements. Traditional keyword search fails when recruiters want to find "someone with startup experience who has managed cross-functional teams." Svod needed a smarter way to search resumes—one that understood context and intent, not just keywords.
- Natural language queries replace complex boolean search strings
- RAG architecture ensures results are grounded in actual resume content
challenge
Problem & Constraints
The client needed a resume search system that could understand queries like "experienced Python developer who has worked in fintech" and return relevant candidates. The solution had to be affordable, easy to use for non-technical HR staff, and provide transparency by showing which parts of a resume matched the query.
- No dedicated infrastructure—needed managed cloud deployment
- Source attribution required for trust and verification
solution
Solution Overview
We built a RAG (Retrieval-Augmented Generation) system that indexes resumes into ChromaDB with semantic embeddings. HR professionals can ask questions in plain Russian and receive ranked candidate matches with highlighted relevant sections from each resume.
- LangChain orchestration handles query understanding and retrieval
- ChromaDB provides fast vector similarity search over resume embeddings
implementation
Implementation Journey
The system parses uploaded resumes (PDF, DOCX), chunks them intelligently, and stores embeddings in ChromaDB. When a user submits a natural language query, LangChain retrieves relevant chunks and presents them with the original resume context. FastAPI provides a clean REST interface for the frontend.
- Resume parsing with robust format handling
- Semantic chunking preserves context boundaries
- OpenAI embeddings capture meaning beyond keywords
- Source attribution shows exactly which resume sections matched
results
Results & Impact
The delivered system allows HR professionals to search resumes using natural language, dramatically reducing time-to-candidate-discovery. The live demo showcases the core functionality and serves as a starting point for future enhancements.
- Affordable solution delivered via Kwork marketplace
- Scalable architecture ready for production HR workflows
Details we can still add
Answering these will let us enrich the manuscript with sharper narrative proof points and concrete outcomes.
- Metrics: What is the average time saved per candidate search compared to manual review?
- Scale: How many resumes can the system handle before performance degrades?
- Features: Would multi-language support (English resumes) add value for the target market?
- Integration: Are there ATS (Applicant Tracking System) integrations the client would prioritize?
Stack at a glance
- Python
- FastAPI
- LangChain
- ChromaDB
- OpenAI Embeddings
- Render
Build your own AI-powered search
We help teams create intelligent search experiences that understand natural language. From resume matching to document discovery, let's build something that actually works.