Screen Resumes Faster with Custom AI Recruiting Automation

Most recruiting teams start with their ATS's built-in filtering tools. These systems rely on basic keyword matching. If a job description asks for "AWS experience" but a great resume lists "Amazon Web Services," the keyword filter misses it. This forces recruiters to manually review hundreds of resumes just to catch simple synonyms, defeating the purpose of automation.

A common next step is a third-party AI screening tool. These tools are one-size-fits-all and their scoring logic is a black box. A tool might rank a candidate with 10 years of network security experience lower than a junior developer for a DevOps role because it overweighted the keyword "Python" from the job description. The recruiter cannot see why a score is 85 vs 65, making it impossible to trust or fine-tune.

These plugins also introduce another per-seat, per-month subscription cost. For a 15-person firm, that can add up to thousands per year for a single feature that isn't tailored to their specific niche, like placing cybersecurity experts where certifications like CISSP are more important than specific keywords.

Syntora's approach to an AI-driven resume screening system would begin with a discovery phase to understand your specific ATS integration points and data security requirements. We would work with your team to establish secure API connections, whether your ATS is Greenhouse, Lever, or Bullhorn. Upon a new job posting or applicant submission, a webhook or scheduled process would trigger the system to pull new applicant resumes in various formats, such as PDFs and DOCX files. For robust text extraction, the system would utilize libraries like `pdfplumber` in a Python environment to clean and extract raw text from each file.

The extracted text would then be sent to the Claude API with a meticulously engineered prompt. This process would return a structured JSON object, normalizing key fields such as work history, skills, and education. Syntora has extensive experience building similar document processing pipelines using Claude API for sensitive financial documents, and the same robust pattern applies to professional resumes. This structured data would then be stored in a secure Supabase Postgres database, creating a rich, queryable candidate profile beyond basic keyword matching.

For candidate matching, the system would generate a vector embedding of each job description using models like `sentence-transformers`. When a recruiter requests a ranked list of candidates, a FastAPI endpoint would compare the job description embedding to the stored candidate embeddings. This involves calculating cosine similarity to identify the most semantically relevant matches, presenting a ranked list tailored to the job's requirements.

The entire service would be architected for deployment on cloud platforms like AWS Lambda, allowing for scalable processing and optimized operational costs. The final ranked score and a concise summary of the candidate's fit would be designed to integrate directly into a custom field within your ATS, ensuring seamless adoption into your existing recruitment workflow. Syntora would deliver the system as a custom engineering engagement, including architectural design, development, testing, and deployment support, with typical build timelines for this complexity ranging from 8-16 weeks depending on integration challenges and required customizations. Clients would need to provide API access to their ATS and collaborate on defining screening criteria.