Get a Custom AI Recruiting System Built for Your Firm
A custom AI recruiting system for a small business is a one-time project cost. Pricing depends on your ATS integration and the complexity of the matching algorithm.
Syntora specializes in building custom AI recruiting systems, offering expertise in technical architecture and data integration for talent acquisition. We provide engineering engagements to solve specific recruiting challenges, focusing on robust, scalable solutions rather than off-the-shelf products.
The final scope for such a system is determined by the volume of historical candidate data and the number of distinct roles your firm hires for. A modern ATS like Greenhouse with two years of clean historical data typically allows for a build timeline of 4-6 weeks for core functionality. Firms with inconsistent data spread across multiple systems would require more extensive upfront data processing and thus a longer engagement.
What Problem Does This Solve?
Most recruiting firms rely on the basic keyword search in their Applicant Tracking System (ATS). This approach fails because it cannot understand context. A search for "AWS" will miss a senior engineer whose resume details experience with "EC2, S3, and Lambda," because the exact acronym "AWS" is not present. This boolean logic forces recruiters to spend hours building complex search strings that still miss qualified candidates.
To fix this, some firms try off-the-shelf AI sourcing tools. These tools are designed for outbound prospecting, not for ranking inbound applicants. They are black boxes that provide a score without explanation, and they cannot be trained on your firm's specific history of successful placements. You end up with a generic model that does not understand what a good candidate looks like for your clients.
A few attempt to build a DIY solution with no-code platforms. A workflow that triggers on a new applicant, uses an OCR tool to parse the resume, and then checks a Google Sheet for 15 keywords can use over 10 tasks per resume. At 400 applicants a month, this becomes 4,000 tasks and a bill that grows with your applicant volume for a slow, brittle process that still relies on simple keyword matching.
How Would Syntora Approach This?
Syntora's approach to building a custom AI recruiting system begins with a discovery phase to understand your specific workflow and ATS setup. We would start by integrating with your existing ATS API. Using Python and the `requests` library, we would pull 18-24 months of historical application data, including resumes and placement outcomes. This data would then be loaded into a dedicated Supabase Postgres instance, establishing a clean, structured dataset of candidates that your firm has previously vetted.
The core matching logic would leverage a sentence-transformer model from the Hugging Face library to convert every resume and job description into a vector embedding. This numerical representation is designed to capture semantic meaning beyond simple keywords. These embeddings would be stored in your Supabase instance using the `pgvector` extension, enabling high-speed similarity searches; we would architect the system to achieve query responses for top candidate matches in under 100ms. Syntora has built similar document processing pipelines using Claude API for financial documents, and the same pattern applies to recruiting documents.
The matching model would be wrapped in a FastAPI service. We would configure a webhook in your ATS (Lever, Greenhouse, Workable) to call this API whenever a new candidate applies. The API would generate an embedding for the new resume, query the database for the best-matching open jobs, and write the results directly into a custom field on the candidate's profile within your ATS. We would design this entire process, from webhook trigger to ATS update, to complete in under 700ms.
For deployment, Syntora would containerize the API with Docker and deploy it to AWS Lambda using the Mangum adapter for serverless execution. This architecture is designed to ensure hosting costs remain minimal, typically under $30/month for most small firms. We would implement structured logging with `structlog` feeding into AWS CloudWatch, allowing for real-time monitoring. The system would be configured with automated alerts for issues like API error rates exceeding 1% or latency surpassing 1.5 seconds, ensuring operational stability.
The deliverables for this engagement would include the deployed AI recruiting system, comprehensive documentation, and knowledge transfer to your team. Your active involvement in providing historical data, access to your ATS, and feedback during development would be crucial to the project's success.
What Are the Key Benefits?
Launch in 4 Weeks, Not 4 Quarters
From the initial data pull to a live, integrated system in 20 business days. Your recruiters start getting ranked candidate matches immediately.
One-Time Build Cost, Near-Zero Hosting
Pay for the engineering project once. The AWS Lambda and Supabase hosting is typically under $30 per month, not a scaling per-recruiter SaaS fee.
You Get the Full GitHub Repository
We deliver the complete Python source code, Dockerfiles, and deployment scripts. You have full ownership to modify or extend the system.
Real-Time Monitoring on All Services
CloudWatch alerts notify us if an ATS API changes or processing fails. We fix issues before your recruiting team notices a problem.
Works Natively Inside Your Current ATS
Candidate scores and job matches appear in custom fields in Greenhouse, Lever, or Workable. No new dashboard for your team to learn.
What Does the Process Look Like?
Week 1: ATS Access and Data Audit
You provide read-only API keys for your ATS. We pull historical data, assess its quality, and deliver a data profile report confirming project viability.
Weeks 2-3: Algorithm and API Build
We build the core matching algorithm and the FastAPI service. You receive a secure link to a demo environment to test the matching logic with real job descriptions.
Week 4: Integration and Deployment
We connect the API to your live ATS using webhooks and deploy the system to AWS Lambda. You receive the complete source code and deployment credentials.
Post-Launch: Monitoring and Handoff
We monitor the live system for 30 days to ensure stability and performance. You receive a detailed runbook with architectural diagrams and maintenance procedures.
Frequently Asked Questions
- What factors change the project cost and timeline?
- The main factors are data quality and integration points. A single, clean data source from a modern ATS is straightforward. Connecting to multiple, older systems or cleaning very inconsistent historical data can add 1-2 weeks to the timeline. The volume of candidates to process for the initial model training also influences the scope. We provide a fixed quote after the data audit.
- What happens if a resume in a strange format fails to parse?
- The system is built with error handling. If a PDF parser fails, the system logs the error, assigns the candidate a neutral score, and sends a notification to a designated Slack channel for manual review. This ensures the entire pipeline does not halt due to one bad file, and your team is aware of the exception.
- How is this different from using a sourcing tool like SeekOut or HireEZ?
- Sourcing tools help you find new candidates outside your network. Our system is built to intelligently rank and match the inbound and existing candidates you already have in your ATS. It solves the 'too many applicants, not enough time' problem by showing recruiters which candidates to focus on first. It complements, not replaces, sourcing.
- How do you handle potential bias in the AI model?
- We proactively mitigate bias by scrubbing Personally Identifiable Information (PII) like names and demographic proxy variables from the data used for model training. The algorithm ranks candidates based on skills and experience relative to the job description. The system provides suggestions; your recruiters always make the final hiring decision, creating a human review gate.
- Do we need an engineer on staff to maintain this?
- No. The system is designed for low maintenance, with automated monitoring and alerting. The post-launch support period covers any initial issues. We provide a detailed runbook that a general Python developer can use for future modifications, but no day-to-day intervention is required to keep the system running.
- How is our candidate data handled and stored?
- Your data is stored in a private, dedicated Supabase Postgres database instance that you control. All data is encrypted at rest and in transit. Syntora interacts with your data via secure, revocable API keys during the build and monitoring period. You retain full ownership and control over your data and the infrastructure it resides on.
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