Build Custom AI Recruiting Workflows
A small company should hire an AI agency when its recruiting process is too complex for standard ATS features. This is when custom resume screening, candidate matching, and personalized outreach are business-critical.
Syntora offers expertise in building custom AI recruiting automation systems, addressing complex applicant screening and candidate matching challenges for small companies. We leverage our experience with document processing pipelines and large language models like Claude API to architect tailored solutions for this industry, focusing on technical capability and a strategic approach rather than relying on prior deployments in this specific vertical.
The scope of an AI recruiting automation build depends on the number of roles, applicant volume, and integration points with your Applicant Tracking System (ATS). A firm with one primary job board feed and a clean Greenhouse instance would represent a faster build. A firm sourcing from LinkedIn, AngelList, and referrals into a complex Lever ATS would require more extensive data mapping and integration work.
What Problem Does This Solve?
Most firms start with their ATS's built-in filters, like in Greenhouse or Lever. These are simple keyword matches. A filter for "Python" and "FastAPI" will find resumes that list both, but it cannot rank them. It treats a candidate with 10 years of Python experience and a 3-month FastAPI project the same as an expert. This creates a high volume of low-quality matches that a human still has to sort through.
Consider a 15-person tech recruiting firm sourcing for a startup client. The client needs a "senior Python developer with FastAPI experience who has worked at a company under 50 people." The ATS filter for "FastAPI" returns 80 candidates. A recruiter must manually open each resume or LinkedIn profile to check their work history for company size. This manual check for a single role takes 2-3 hours and has to be repeated every week.
This is a design limitation. These platforms are databases with search, not intelligent matching systems. They cannot combine signals, score candidates on a spectrum, or personalize outreach based on a candidate's specific project history. The logic is rigid: a candidate either matches a keyword or they do not. This binary approach breaks down for nuanced, high-value roles.
How Would Syntora Approach This?
Syntora would start by auditing your existing applicant data and ATS integration points, whether you use Lever, Greenhouse, or Ashby. This initial discovery phase would allow us to understand your specific data structure and needs. The approach would involve connecting to your ATS API to securely pull relevant applicant data. For efficient data retrieval, we typically utilize Python's httpx library for async requests. Each resume PDF or DOCX would be parsed using PyMuPDF to extract raw text, which we would then store in a structured Supabase Postgres database. This process creates a clean, normalized dataset essential for effective model training. We have experience building similar document processing pipelines using Claude API for sensitive financial documents, and the same robust pattern applies to recruiting documents.
For each open role, the system would utilize your job description and a set of "gold standard" resumes you provide to inform model behavior via the Claude API. The core matching logic would be built in a FastAPI application, designed to generate a comprehensive set of features for each new candidate and rank them based on alignment with the role's requirements.
For highly matched candidates, the system would be capable of generating personalized outreach emails. This functionality would leverage the Claude API to highlight specific candidate alignments discovered during the screening process, for example, noting "your experience leading FastAPI migrations aligns directly with our client's current project." Structured logging via structlog would be implemented to track every generated message, enabling quality control and continuous improvement.
The entire system would be deployed as a set of serverless functions on AWS Lambda, triggered by webhooks from your ATS when a new candidate applies. The process would write the match score and a suggested outreach email back to a custom field in your ATS. Hosting costs for such an architecture are typically under $50/month. A simple front-end dashboard built with Vercel would provide system status and insights. Typical build timelines for an initial system of this complexity range from 8 to 12 weeks, depending on the number of integration points and the cleanliness of existing data. The client would need to provide access to their ATS API, historical applicant data, and a selection of example "gold standard" resumes for each role. Deliverables would include the deployed AI system, source code, and comprehensive documentation.
What Are the Key Benefits?
Scores in Seconds, Not Hours
Reduce initial resume screening time from 4 hours per day to under 5 minutes. The system screens 500 resumes in 90 seconds.
Pay for the Build, Not the Seat
A one-time project cost with fixed monthly maintenance. No per-recruiter SaaS fees that penalize you for growing your team.
You Own the GitHub Repo
We deliver the complete Python source code and deployment scripts. You get full ownership, no vendor lock-in, and can modify it later.
Alerts Before Your Recruiters Notice
We configure CloudWatch alarms that trigger Slack alerts if the ATS API connection fails or processing errors exceed 1%. Issues are flagged in minutes.
Writes Directly Into Your ATS
Integrates with Lever, Greenhouse, and Ashby via their native APIs. Scores and outreach drafts appear in the tools your team already uses.
What Does the Process Look Like?
Scoping & ATS Access (Week 1)
You provide read-only API credentials for your ATS. We audit your data structure and define the exact matching criteria for 1-2 pilot roles. You receive a technical spec document.
Core Model & API Build (Week 2)
We build the FastAPI application for screening and ranking. You receive a link to a staging environment where you can test the matching logic with sample resumes.
Integration & Deployment (Week 3)
We connect the API to your live ATS using webhooks and deploy the system to AWS Lambda. You receive a private GitHub repository with the full source code.
Monitoring & Handoff (Week 4)
We monitor the live system for one week, tuning as needed. You receive a runbook with instructions for common issues and a support plan for ongoing maintenance.
Frequently Asked Questions
- How much does a custom recruiting automation system cost?
- The cost depends on the number of roles to automate and the complexity of your ATS data. A project for screening candidates for 2-3 similar roles typically takes 3-4 weeks. After the initial build, a flat monthly fee covers hosting and maintenance. We can provide a fixed-price quote after a 30-minute discovery call where we review your specific workflow.
- What happens if the AI makes a bad match or the system goes down?
- The system is designed with human review gates. It suggests a rank, but a recruiter makes the final call. If the AWS Lambda function fails, it has a built-in retry mechanism. If it fails three times, it sends a Slack alert with the error details. Candidates are simply queued for the next successful run; no data is lost.
- How is this different from using a tool like SourceWhale or Gem?
- SourceWhale and Gem are excellent for managing outreach sequences and tracking engagement. They are not candidate matching systems. Syntora builds the underlying intelligence that decides *who* to contact. Our system identifies the top 10% of applicants, and then a tool like Gem can be used to manage the outreach campaign to them.
- How do you handle potential AI bias in screening?
- We explicitly exclude demographic data like names, photos, and graduation years from the model's features. The matching is based on skills, experience, and project outcomes. We also provide a feature importance report, showing you exactly which criteria the model used for its rankings, ensuring transparency and allowing for audits.
- Who owns the candidate data, and how is it secured?
- You own all data. We process it within our secure AWS environment and store it in a dedicated Supabase instance for your project only. All data is encrypted at rest and in transit. At the end of the engagement, we can transfer the database to your own AWS account or securely delete it upon your request.
- What happens if we migrate from Greenhouse to Lever next year?
- Since we build with a modular adapter pattern, changing the ATS is straightforward. We would write a new adapter for the Lever API, which is typically a 3-5 day engagement. The core matching logic, written in Python and FastAPI, remains unchanged. You are not locked into a specific ATS vendor.
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