Screen Resumes Faster with Custom AI Recruiting Automation
Recruiting firms use AI to automatically parse and structure resume data. This data then powers algorithms that rank candidates against job descriptions.
Syntora designs and builds custom AI solutions for recruiting firms seeking to enhance resume screening. Their approach involves expert engineering of pipelines that use tools like Claude API and FastAPI to structure applicant data and provide semantic candidate matching, delivered as a comprehensive engagement rather than a pre-built product.
The complexity of building such a system depends on factors like your Applicant Tracking System (ATS), the volume of resumes processed, and the specialization of roles you recruit for. Integrating with a modern ATS via API, like Greenhouse or Lever, is generally straightforward. Custom parsing logic may be required for older, on-premise systems or for highly specialized technical resumes with unique formatting. Syntora would start by auditing your existing workflow and ATS to define the optimal architectural approach for your specific needs.
What Problem Does This Solve?
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.
How Would Syntora Approach This?
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.
What Are the Key Benefits?
Get a Shortlist in Minutes, Not Days
Process 500 new applicants for a role and get a ranked top-20 list back in the ATS in under 15 minutes. Stop wasting the first day of a search on manual triage.
Pay Once for the System, Not Per Recruiter
A single project cost for a system you own. Avoids monthly per-seat subscription fees from third-party tools that penalize you for growing your recruiting team.
You Own the Code and Ranking Logic
We deliver the full Python source code in your private GitHub repository. You can modify the scoring logic for niche roles at any time without asking a vendor.
Monitoring Tells You When It's Working
The system logs every run to Supabase and can send a daily summary to Slack. Get immediate alerts if the ATS API connection fails or parsing errors exceed a threshold.
Works Inside Your Current ATS
Connects directly to Greenhouse, Lever, or Bullhorn APIs. Recruiters see scores and summaries in the candidate profiles they already use every day.
What Does the Process Look Like?
Discovery and ATS Access (Week 1)
You provide read-only API keys for your ATS and 3-5 sample job descriptions. We audit your data structure and deliver a detailed technical plan for the build.
Core Engine Build (Weeks 2-3)
We build the resume parsing and ranking logic using Python and the Claude API. You receive a demo script to test against 10 sample resumes and verify the output.
Integration and Deployment (Week 4)
We deploy the system on AWS Lambda and configure the webhooks to your ATS. You get a private link to the live monitoring dashboard to see real-time processing.
Live Monitoring and Handoff (Weeks 5-8)
We monitor the system live for 4 weeks, tuning prompts and ranking logic based on your feedback. You receive the full source code and a runbook for ongoing maintenance.
Frequently Asked Questions
- How much does a custom resume screening system cost?
- The cost depends on the ATS integration, resume volume, and the number of distinct role types to model. A typical build for a firm with under 20 recruiters using a standard ATS like Greenhouse is a fixed-scope project. We can provide a detailed quote after a 30-minute discovery call where we review your current workflow.
- What happens if the AI ranks a great candidate low?
- The system presents a ranked list, not a final decision. Recruiters can always view all applicants and can override any score. Every manual override is logged and used as feedback data, so the system learns from its mistakes over time. The goal is to assist, not replace, a recruiter's professional judgment.
- How is this different from my ATS's built-in AI features?
- Most ATS tools rely on simple keyword matching. Our system uses language models to understand context, qualifications, and experience semantically. We also build custom ranking logic tailored to your firm's specific evaluation criteria for different roles, something an off-the-shelf ATS feature cannot do. You get a system that recruits your way.
- What if we change our Applicant Tracking System later?
- The core parsing and ranking engine is built independently of the ATS. The integration points are isolated in their own software module. Migrating from Greenhouse to Lever, for example, would require rewriting only that one module, which is a small project, not a full rebuild from scratch. You own the core system.
- Can the system handle resumes in languages other than English?
- Yes. The Claude API has strong multilingual capabilities. We can configure the parsing prompts to handle resumes in Spanish, French, German, and more. During discovery, we test its performance on your sample non-English resumes to confirm accuracy before starting the build. This is a key advantage over many standard recruiting tools.
- What does ongoing maintenance look like after handoff?
- The system is designed for low maintenance, running on serverless AWS Lambda. The main task is periodic prompt tuning, perhaps once a quarter, to refine performance. The runbook we provide covers this process. For firms that prefer a hands-off approach, we offer an optional support plan that includes proactive monitoring and quarterly tuning sessions.
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