Build a Custom AI Recruiting System That Works
Choose off-the-shelf software for simple needs like interview scheduling. Choose a custom build for core business processes like candidate screening and matching.
Syntora develops custom AI recruiting systems by applying advanced natural language processing and machine learning techniques to candidate screening and matching. Syntora would architect a solution that integrates with existing ATS platforms, leveraging technologies like Claude API and FastAPI to refine candidate selection workflows. The goal is to enhance recruiter efficiency through intelligent, data-driven candidate analysis.
The decision between off-the-shelf and a custom system for AI recruiting depends heavily on your existing Applicant Tracking System (ATS) and the unique hiring volume and complexity of the roles you fill. Syntora approaches custom AI recruiting solutions by first auditing your current ATS integration needs. For modern ATS platforms like Greenhouse, direct API connections are typically straightforward. However, integrating with legacy systems or multiple job boards often requires developing more custom logic to normalize diverse data sources, a core part of our initial discovery process. This tailored approach ensures the system precisely addresses your firm's specific challenges and scales with your operational demands.
What Problem Does This Solve?
Most small recruiting teams try using the built-in AI features of their Applicant Tracking System. These tools are often one-size-fits-all, trained on generic data from thousands of other companies. They can match keywords like "Python" but cannot understand the nuance that your firm needs a Python developer with specific fintech compliance experience. This generic matching creates noise, not signal.
A 20-person tech consultancy we spoke with used their ATS's "AI matching" for a Senior Developer role and got 250 applicants. The tool flagged 150 candidates who had the right keywords, but it could not distinguish between a 3-month bootcamp graduate and a developer with 5 years of experience building payment gateways. The recruiters still had to manually review 150 resumes to find the 10 qualified people, defeating the purpose of the automation.
Larger AI platforms like Eightfold or Paradox are built for enterprises with thousands of employees and charge high per-employee-per-year fees. They require long implementation cycles and dedicated admins. For an SMB, this approach is too expensive and complex for a single, critical workflow like candidate screening.
How Would Syntora Approach This?
Syntora's approach to building a custom AI recruiting system begins with a comprehensive discovery phase. This phase includes auditing your existing Applicant Tracking System (ATS) and establishing secure API connections, using libraries like httpx for asynchronous requests to platforms such as Greenhouse or Lever. The next step involves pulling historical data, typically 12-18 months of job descriptions and associated candidate resumes, to create a robust dataset. This corpus, often comprising 5,000 to 10,000 documents, would be loaded into a Supabase Postgres database, serving as the ground truth for training the AI model.
The system would then leverage the Claude API's function calling capabilities to parse unstructured resume text and job descriptions. This process extracts over 50 structured features, including years of experience, specific skills with proficiency levels, and educational background. Subsequently, these candidate features are compared against job requirements using vector embeddings. For optimal performance, the underlying model would be fine-tuned on your organization's past successful hires, enabling it to learn the specific attributes that define ideal candidates for your unique roles.
The trained model would be packaged into a container and deployed as a FastAPI service on AWS Lambda, ensuring scalability and efficient processing. Upon a new candidate application within your ATS, a webhook would trigger this Lambda function. The entire process, from application receipt to writing a match score back to a custom ATS field, is designed for high efficiency.
Crucially, this AI system is intended to augment, not replace, human recruiters by providing a focused list of top-ranked candidates for immediate human review within the ATS. Syntora would implement a robust structlog-based logging pipeline to AWS CloudWatch. This pipeline would track model predictions against recruiter feedback, establishing an iterative feedback loop. This feedback mechanism allows for periodic model retraining, typically every 90 days, to adapt to evolving hiring criteria and market dynamics, ensuring the system remains current and effective.
A typical engagement for such a custom system might take 10-16 weeks to build and deploy, requiring the client to provide access to their ATS, historical data, and active participation in defining success metrics and providing feedback during the development and post-deployment phases. Deliverables would include the deployed AI system, source code, documentation, and a training plan for internal teams. We've built similar document processing pipelines using Claude API for financial documents, and the same architectural patterns apply effectively to recruiting documents.
What Are the Key Benefits?
Get Candidate Rankings in 4 Weeks, Not 4 Quarters
From initial ATS connection to a live candidate scoring system in 20 business days. Your team sees value immediately, not after a lengthy enterprise rollout.
Fixed Project Cost, Not Per-Seat SaaS Fees
A one-time build cost with minimal monthly AWS hosting fees. You are not penalized with a higher bill for growing your recruiting team.
You Get the Keys to the Codebase
We deliver the complete Python source code in your private GitHub repository, along with detailed documentation. Your system is an asset, not a rental.
Alerts When Your Model Needs a Tune-Up
We configure CloudWatch alerts that notify you if scoring accuracy degrades or APIs fail. The system is monitored 24/7 without manual checks.
Works Inside Your Existing ATS
Scores appear as a native custom field in Greenhouse, Lever, or Ashby. Your recruiters stay in one system without learning a new, separate platform.
What Does the Process Look Like?
Week 1: ATS Connection and Data Audit
You provide read-only API keys to your ATS. We pull historical data and deliver a data quality report outlining the suitability for model training.
Week 2: Model Prototyping
We build and train the initial scoring model. You receive a model performance summary showing how well it ranks past successful and unsuccessful candidates.
Weeks 3-4: API Build and Integration
We deploy the scoring API and connect it to your ATS via webhooks. We deliver a short video walkthrough showing your team how to use the new scores.
Weeks 5-8: Monitoring and Handoff
We monitor live performance for 30 days post-launch to tune the model. You receive a final runbook with instructions for monitoring and future retraining.
Frequently Asked Questions
- How much does a custom AI recruiting system cost?
- The cost depends on the complexity of your ATS integration and the quality of your historical data. A project for a firm with clean data in a modern ATS like Greenhouse is straightforward. A system integrating multiple sources or requiring significant data cleanup is more involved. We provide a fixed-price quote after a 30-minute discovery call where we review your exact needs.
- What happens if the scoring API goes down?
- The system is designed for graceful failure. If the AWS Lambda function fails, the webhook from your ATS will time out, and no score will be written. The candidate profile remains untouched. We receive an immediate CloudWatch alert and typically restore service within an hour. This is covered by our standard 30-day post-launch support, with optional monthly retainers available after.
- How is this different from using an add-on like Gem or SourceWhale?
- Gem and SourceWhale are excellent for candidate outreach and email sequencing. They help recruiters engage with candidates you have already found. Syntora's systems work one step earlier. We help you find the best candidates from the inbound applicant pool automatically, so your recruiters spend their time engaging with the right people from the start.
- Can the system screen for things other than skills?
- Yes. The model can be trained to rank based on any data in your ATS or resumes. We have built systems that screen for specific certifications, security clearances, language proficiency, or even proximity to an office location. During discovery, we map out the top 3-5 hiring criteria that are most important for your roles and ensure the model prioritizes them.
- Is this system biased?
- We design the system to be bias-aware. We explicitly exclude demographic information like name and age from the model's features. The output is a score based only on skills and experience relative to the job description. The final hiring decision always remains with a human recruiter, using the AI score as one of many inputs. We provide a feature importance report so you can see what the model values.
- What kind of team is Syntora a good fit for?
- Syntora is ideal for recruiting firms or SMBs with 5-50 employees who have a repeatable, high-volume hiring process but lack a dedicated engineering team. If you are manually screening more than 200 resumes a month and feel that off-the-shelf software is too generic or expensive for your specific needs, a custom build is likely the right path. Book a discovery call at cal.com/syntora/discover.
Ready to Automate Your Professional Services Operations?
Book a call to discuss how we can implement ai automation for your professional services business.
Book a Call