Build a Custom AI Recruiting System That Works

Most small recruiting teams try using the built-in AI features of their Applicant Tracking System. These tools are often one-size-fits-all, trained on generic data from thousands of other companies. They can match keywords like "Python" but cannot understand the nuance that your firm needs a Python developer with specific fintech compliance experience. This generic matching creates noise, not signal.

A 20-person tech consultancy we spoke with used their ATS's "AI matching" for a Senior Developer role and got 250 applicants. The tool flagged 150 candidates who had the right keywords, but it could not distinguish between a 3-month bootcamp graduate and a developer with 5 years of experience building payment gateways. The recruiters still had to manually review 150 resumes to find the 10 qualified people, defeating the purpose of the automation.

Larger AI platforms like Eightfold or Paradox are built for enterprises with thousands of employees and charge high per-employee-per-year fees. They require long implementation cycles and dedicated admins. For an SMB, this approach is too expensive and complex for a single, critical workflow like candidate screening.

Syntora's approach to building a custom AI recruiting system begins with a comprehensive discovery phase. This phase includes auditing your existing Applicant Tracking System (ATS) and establishing secure API connections, using libraries like httpx for asynchronous requests to platforms such as Greenhouse or Lever. The next step involves pulling historical data, typically 12-18 months of job descriptions and associated candidate resumes, to create a robust dataset. This corpus, often comprising 5,000 to 10,000 documents, would be loaded into a Supabase Postgres database, serving as the ground truth for training the AI model.

The system would then leverage the Claude API's function calling capabilities to parse unstructured resume text and job descriptions. This process extracts over 50 structured features, including years of experience, specific skills with proficiency levels, and educational background. Subsequently, these candidate features are compared against job requirements using vector embeddings. For optimal performance, the underlying model would be fine-tuned on your organization's past successful hires, enabling it to learn the specific attributes that define ideal candidates for your unique roles.

The trained model would be packaged into a container and deployed as a FastAPI service on AWS Lambda, ensuring scalability and efficient processing. Upon a new candidate application within your ATS, a webhook would trigger this Lambda function. The entire process, from application receipt to writing a match score back to a custom ATS field, is designed for high efficiency.

Crucially, this AI system is intended to augment, not replace, human recruiters by providing a focused list of top-ranked candidates for immediate human review within the ATS. Syntora would implement a robust structlog-based logging pipeline to AWS CloudWatch. This pipeline would track model predictions against recruiter feedback, establishing an iterative feedback loop. This feedback mechanism allows for periodic model retraining, typically every 90 days, to adapt to evolving hiring criteria and market dynamics, ensuring the system remains current and effective.

A typical engagement for such a custom system might take 10-16 weeks to build and deploy, requiring the client to provide access to their ATS, historical data, and active participation in defining success metrics and providing feedback during the development and post-deployment phases. Deliverables would include the deployed AI system, source code, documentation, and a training plan for internal teams. We've built similar document processing pipelines using Claude API for financial documents, and the same architectural patterns apply effectively to recruiting documents.