Build an AI Resume Screener That Works Like Your Best Recruiter

Many recruiting firms rely on their Applicant Tracking System's (ATS) built-in keyword search. Tools like Greenhouse or Lever let you filter for terms like "Python" or "SaaS sales". But this is brittle. It misses candidates who list "Django" (a Python framework) and ranks resumes that mention "Python" once higher than an expert who lists it in multiple projects without repeating the keyword.

Consider a search for a "Senior Product Manager" requiring "B2B SaaS experience". A keyword search returns a candidate who once worked for a company that sold SaaS, but whose role was in HR. It misses a perfect candidate whose resume says they "managed a B2B product portfolio" and "grew ARR by 200%". The recruiter still has to manually read every resume the keyword search returns, which adds hours of low-value work each week.

Off-the-shelf AI screening tools promise a quick fix, but they often operate as black boxes. They provide a score without explaining why a candidate is a 9/10 or a 4/10. These tools also struggle with niche roles. A generic model trained on millions of resumes does not understand that for your client, "cold-chain logistics" is a non-negotiable skill, not just another industry keyword.

Syntora's approach to an AI resume screening system begins with an in-depth discovery phase to understand your current recruitment workflow, specific job requirements, and existing applicant tracking system (ATS). This phase involves assessing your ATS API for integration points and reviewing sample job descriptions and anonymized resumes.

The technical architecture would typically involve several key components. For document processing, Python libraries like pdfplumber are effective for extracting raw text from standard PDF resumes, normalizing varied formats into a consistent, structured data representation. This data would then be stored in a suitable database, such as Supabase Postgres. Syntora has built document processing pipelines using the Claude API for complex financial documents, and the same pattern applies to analyzing resume content.

The core of the system's intelligence would move beyond simple keyword matching. We would configure the Claude API to perform advanced entity extraction, precisely identifying skills, relevant years of experience, and specific project outcomes from each resume. Concurrently, for each job description, a "requirements vector" would be constructed, allowing for weighted importance of mandatory versus preferred skills. A FastAPI endpoint would be engineered to accept new resumes, generate a corresponding candidate vector, and calculate a similarity score against the job's vector.

The delivered system would not only score but also rank candidates, processing a batch of applicants for a single job requirement efficiently. The output would be a prioritized list, which Syntora could integrate directly into a custom field within your ATS or present through a simple web dashboard built with Vercel. Each candidate entry would include a score, a concise summary of their fit, and a clear list of matched and missing skills, enabling recruiters to focus on a targeted shortlist.

For system deployment, a serverless function architecture on AWS Lambda offers a cost-effective and scalable solution, suitable for processing high volumes of resumes. Syntora would implement robust monitoring and error handling, including structured logging with structlog to track requests and set up alerts for events like Claude API failures or unparseable resumes. This ensures operational stability and allows for proactive issue resolution.

A typical engagement for this type of system would include the discovery phase, architecture design, custom development, testing with provided data, deployment, and documentation. The client would be expected to provide access to their ATS, sample data (job descriptions and anonymized resumes), and participate in regular feedback sessions. Deliverables would include the deployed and tested system, source code, and comprehensive technical documentation.