Build an AI Resume Screener That Works Like Your Best Recruiter
AI automation screens resumes by extracting skills and ranking candidates against job criteria. This reduces manual review time from minutes to seconds per applicant.
Syntora designs custom AI automation systems to screen resumes faster for consultancies and recruiting agencies. Our approach involves detailed discovery, expert architecture using tools like Claude API and FastAPI, and integration with existing ATS platforms to deliver a ranked shortlist of candidates.
Syntora designs and implements custom AI-powered resume screening systems. The scope and timeline of an engagement depend on several factors: the volume of resumes, the number of distinct roles, the quality and format of incoming documents (e.g., standard PDFs versus scanned images), and the need for integration with existing applicant tracking systems. For example, a system designed to process 500 PDF resumes a month for up to 10 distinct roles represents a common project scope, typically requiring an 8-12 week build. Parsing scanned image-based resumes or integrating with a legacy, on-premise ATS would require a more tailored development approach.
What Problem Does This Solve?
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.
How Would Syntora Approach This?
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.
What Are the Key Benefits?
Get a Ranked Shortlist in 90 Seconds
Stop spending hours on manual review. For any new job, get a ranked list of the top 20% of applicants with fit justifications in under two minutes.
One-Time Build, Predictable Hosting
No per-seat licenses or per-resume fees. After the initial build, your monthly hosting and API costs are typically under $100, regardless of team size.
You Own the Code and the Logic
You receive the full Python source code in a private GitHub repository. The matching logic is yours to inspect, modify, and extend as your firm grows.
Alerts for Unparseable Resumes
The system monitors itself. If a resume PDF is corrupted or an API key expires, you get a Slack alert with the applicant ID for manual review.
Works Inside Your Current ATS
Scores and rankings appear as custom fields directly in your existing ATS like Bullhorn or Greenhouse. No new platform for your recruiters to learn.
What Does the Process Look Like?
Discovery and ATS Access (Week 1)
You provide API credentials to your ATS and a sample of 10 job descriptions with corresponding good-fit resumes. We define the exact matching criteria for your top 3 roles.
Core Model Build (Week 2)
We build the resume parsing and candidate ranking engine. You receive a demo link to test the system with a sample set of 20 resumes and provide feedback on scoring accuracy.
Integration and Deployment (Week 3)
We connect the system to your live ATS, writing scores back to custom fields. You receive runbook documentation covering the full architecture and data flow.
Monitoring and Handoff (Weeks 4-6)
We monitor the system in production, tuning the logic based on recruiter feedback. After a 2-week stability period, we transfer full ownership and provide a support plan.
Frequently Asked Questions
- How much does a custom resume screener cost?
- The cost depends on the number of integrations and the complexity of your resume formats. A system for a single ATS with standard PDF resumes has a lower scope than one that needs to parse image-based resumes from three different sources. We provide a fixed-price quote after a 30-minute discovery call where we review your exact needs. The typical build timeline is 3-4 weeks.
- What happens when a resume has a weird format the AI cannot read?
- If our pdfplumber script cannot extract text, the system flags the resume with an 'unparseable' status in your ATS and sends a Slack alert with the applicant's name. This ensures no candidate is lost due to a technical glitch. The recruiter can then review that specific resume manually. This happens with less than 1% of modern resumes.
- How is this different from using an off-the-shelf tool like HireEZ?
- HireEZ is primarily a sourcing tool for finding candidates, with some screening features. Syntora builds a system tailored to your inbound applicants and your specific definition of a good fit. We do not charge per seat, and you own the underlying code. Our system is designed to integrate deeply with your existing workflow, not replace it with another platform.
- Does the AI introduce bias into our hiring process?
- We design the system to be bias-aware. The AI is instructed to ignore demographic information like names, locations, and graduation years. It focuses only on skills, experience, and quantifiable achievements relevant to the job description. The final decision is always made by a human recruiter, using the AI's output as a starting point for their review.
- How do you handle sensitive candidate data?
- Candidate data is processed in memory and never stored long-term on our systems. We access your ATS via its official API using short-lived tokens. All data transfer is encrypted with TLS 1.2. The system we build runs in your own AWS account or ours, as you prefer, giving you full control over the data environment. We are happy to sign a DPA.
- Can we adjust the ranking criteria for different roles?
- Yes. The system is not one-size-fits-all. Each job description generates its own unique ranking model. You can specify 'must-have' vs. 'nice-to-have' skills for each role. For example, a 'Senior Engineer' role can be configured to heavily weigh 'system design experience' while a 'Junior Engineer' role prioritizes 'CS degree' and 'internship projects'.
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