Get a Custom AI Recruiting System Built for Your Firm

Most recruiting firms rely on the basic keyword search in their Applicant Tracking System (ATS). This approach fails because it cannot understand context. A search for "AWS" will miss a senior engineer whose resume details experience with "EC2, S3, and Lambda," because the exact acronym "AWS" is not present. This boolean logic forces recruiters to spend hours building complex search strings that still miss qualified candidates.

To fix this, some firms try off-the-shelf AI sourcing tools. These tools are designed for outbound prospecting, not for ranking inbound applicants. They are black boxes that provide a score without explanation, and they cannot be trained on your firm's specific history of successful placements. You end up with a generic model that does not understand what a good candidate looks like for your clients.

A few attempt to build a DIY solution with no-code platforms. A workflow that triggers on a new applicant, uses an OCR tool to parse the resume, and then checks a Google Sheet for 15 keywords can use over 10 tasks per resume. At 400 applicants a month, this becomes 4,000 tasks and a bill that grows with your applicant volume for a slow, brittle process that still relies on simple keyword matching.

Syntora's approach to building a custom AI recruiting system begins with a discovery phase to understand your specific workflow and ATS setup. We would start by integrating with your existing ATS API. Using Python and the `requests` library, we would pull 18-24 months of historical application data, including resumes and placement outcomes. This data would then be loaded into a dedicated Supabase Postgres instance, establishing a clean, structured dataset of candidates that your firm has previously vetted.

The core matching logic would leverage a sentence-transformer model from the Hugging Face library to convert every resume and job description into a vector embedding. This numerical representation is designed to capture semantic meaning beyond simple keywords. These embeddings would be stored in your Supabase instance using the `pgvector` extension, enabling high-speed similarity searches; we would architect the system to achieve query responses for top candidate matches in under 100ms. Syntora has built similar document processing pipelines using Claude API for financial documents, and the same pattern applies to recruiting documents.

The matching model would be wrapped in a FastAPI service. We would configure a webhook in your ATS (Lever, Greenhouse, Workable) to call this API whenever a new candidate applies. The API would generate an embedding for the new resume, query the database for the best-matching open jobs, and write the results directly into a custom field on the candidate's profile within your ATS. We would design this entire process, from webhook trigger to ATS update, to complete in under 700ms.

For deployment, Syntora would containerize the API with Docker and deploy it to AWS Lambda using the Mangum adapter for serverless execution. This architecture is designed to ensure hosting costs remain minimal, typically under $30/month for most small firms. We would implement structured logging with `structlog` feeding into AWS CloudWatch, allowing for real-time monitoring. The system would be configured with automated alerts for issues like API error rates exceeding 1% or latency surpassing 1.5 seconds, ensuring operational stability.

The deliverables for this engagement would include the deployed AI recruiting system, comprehensive documentation, and knowledge transfer to your team. Your active involvement in providing historical data, access to your ATS, and feedback during development would be crucial to the project's success.