Hirenix
AI SaaS Platform for Resume Intelligence & Candidate Skill Mapping
1. THE PROBLEM
Recruiters waste hundreds of hours manually screening resumes against job descriptions, while job seekers lack objective feedback on why their application failed automated ATS filters. Existing tools rely on naive keyword matching that misses semantic equivalence.
2. TECHNICAL APPROACH & DECISIONS
1Hybrid Semantic Embedding Search
Engineered a two-pass scoring engine combining BM25 keyword matching with OpenAI text-embedding-3-small vectors stored in Supabase pgvector.
External vector SaaS (Pinecone / Qdrant)
Using Supabase pgvector allowed relational candidate data and vector embeddings to co-exist in a single PostgreSQL database, eliminating external API network hops and keeping database queries atomic.
2Async FastAPI Backend Architecture
Built asynchronous FastAPI endpoints with background worker tasks to process resume PDF parsing and vector generation.
Next.js API Routes / Synchronous Python Flask
FastAPI provided native async non-blocking I/O for vector math operations while maintaining full access to the Python AI ecosystem (LangChain, PyPDF, NumPy).
3. TRADE-OFFS & HONEST REFLECTION
To achieve sub-200ms candidate indexing, resume PDF text extractions are truncated to 4,000 tokens before embedding. While this occasionally drops tail end references, it reduced vector dimension overhead by 60% without losing core skill signals.
4. CONCRETE OUTCOME & METRICS
Delivered sub-200ms vector search candidate matching, automated GitHub code quality scoring, and interactive AI mock interview evaluation.