Build Custom Candidate Matching for Specialized Roles

Most recruiting firms rely on the built-in keyword search of their Applicant Tracking System (ATS). This method is brittle. A search for "Python developer" will miss a perfect candidate whose resume lists extensive experience with "Django" and "FastAPI" but omits the word "Python". This creates an endless stream of false negatives, burying your best candidates.

A 12-person firm needs to fill a "Senior Site Reliability Engineer" role. Their ATS search for "Kubernetes" and "Terraform" misses a candidate whose resume details a 3-year project migrating an application to AWS EKS using Infrastructure as Code. The resume uses the terms "container orchestration" and "IaC" instead of the exact keywords. The recruiter wastes 8 hours manually reviewing 200 resumes to find someone who was already in their system.

These tools fail because they treat resumes as bags of words, not as summaries of experience. They cannot infer that experience with FastAPI implies proficiency in Python, or that AWS EKS is a managed Kubernetes service. They only match exact character strings, forcing recruiters to play a frustrating guessing game with keywords.

Syntora would approach the development of a custom candidate matching system with a structured engineering engagement. The initial step involves a comprehensive data audit and ingestion strategy, collaboratively designed with your team. We would connect to your existing ATS APIs, such as Lever, Greenhouse, or Ashby, to pull historical job descriptions and candidate resumes. For any unstructured data, like PDF resumes in shared folders, we would implement a Python-based parsing solution, potentially leveraging libraries like PyMuPDF to extract raw text, creating a unified dataset suitable for processing.

Following data ingestion, the process would move to transforming this raw data into a semantically rich format. We would employ a sentence-transformer model, such as all-MiniLM-L6-v2, to convert each resume and job description into high-dimensional vector embeddings. These embeddings capture the contextual meaning, enabling semantic search beyond simple keyword matching. These vectors would be stored in a Supabase Postgres database, utilizing the pgvector extension for efficient similarity search queries.

The core of the system would be a FastAPI service. This service would generate an embedding for a new job description and then query the pgvector index to identify the most semantically similar candidate profiles. For a deeper, qualitative analysis, the system would integrate the Claude API to extract structured skills from both job requirements and candidate profiles. This capability allows for precise skills gap analysis, highlighting what a candidate possesses versus what the role demands. We've built similar document processing pipelines using the Claude API for complex financial documents, and the same robust pattern applies to candidate profiles in the recruiting domain.

Deployment would typically involve containerizing the FastAPI service and deploying it on a cloud platform like AWS Lambda, accessible via an API Gateway. This architecture provides scalability and cost efficiency, with typical hosting costs for services of this nature being highly optimized. The delivered system would expose endpoints for integration into your existing recruitment workflows or a custom front-end interface built on platforms like Vercel, allowing recruiters to review ranked candidates, analyze skill gaps, and easily advance suitable candidates within your ATS. A typical build of this complexity would range from 8-16 weeks, requiring active collaboration and data provision from the client.