Automate Candidate Sourcing with Custom AI Systems
AI for candidate sourcing automates resume screening and ranks applicants against job requirements. This surfaces qualified candidates in minutes and reduces recruiter time spent on manual review.
Syntora offers custom AI engineering engagements for challenges like candidate sourcing in recruiting firms. We design and build robust systems using modern NLP techniques, vector databases, and scalable cloud architectures to automate resume screening and enhance candidate ranking. Our focus is on delivering tailored solutions that integrate seamlessly with existing ATS platforms.
The scope of a system depends on your applicant volume and the structure of your Applicant Tracking System (ATS). A firm using a modern ATS like Greenhouse with structured data is a straightforward build. A team managing applicants in spreadsheets and shared inboxes requires more initial data processing.
What Problem Does This Solve?
Most small recruiting firms rely on the built-in search features of their ATS. These tools use simple keyword matching, which is brittle and misses context. A search for "JavaScript" will not find a senior frontend developer whose resume lists "React" and "ES6" but omits the base term, causing you to miss a perfect-fit candidate.
A common workaround is to build complex boolean search strings, like `("sales" OR "BDM") AND "SaaS" NOT "intern"`. A recruiter at a 10-person firm might spend an hour crafting this for a single role. When they get 200 applicants, the query returns 90 resumes. They then spend three hours manually reading through them, only to discover most lack the required 5 years of closing experience. The initial search gave them volume, not quality.
This entire approach is flawed because keyword and boolean tools cannot grasp semantic meaning. They cannot infer that experience at a major tech company is more relevant for a senior role than a coding bootcamp project. They treat every word equally, which creates significant noise and forces recruiters to waste hours on low-value screening instead of talking to qualified people.
How Would Syntora Approach This?
Syntora approaches AI candidate sourcing as a custom engineering engagement. The initial phase involves a deep dive into your existing Applicant Tracking System (ATS), whether it's Greenhouse, Lever, or Bullhorn, and understanding your specific hiring workflows. This discovery includes defining your data landscape, identifying integration points, and establishing clear success metrics.
The first technical step would be to connect to your ATS API to extract historical job descriptions and successful candidate resumes, forming a critical ground-truth dataset. Resumes would be meticulously parsed into clean, structured text using Python libraries like python-docx and pypdf, then securely stored in a scalable database such as Supabase Postgres.
Leveraging this prepared data, the core of the system would utilize advanced sentence-transformer models to create vector embeddings for all resumes and job descriptions. This allows for the capture of deep semantic meaning. When a new role is posted, the system would efficiently query your talent pool via a pgvector index to identify the most semantically similar candidates. A custom-trained, second-stage ranking model, informed by your past successful hires, would then refine this initial list to produce a highly relevant, ordered ranking.
This entire candidate sourcing workflow would be orchestrated by a robust Python service built with FastAPI. The application would be packaged into a Docker container and deployed as a serverless function, for instance on AWS Lambda, ensuring both cost-efficiency and scalability. An ATS webhook would trigger the function upon a new candidate application, enabling rapid resume processing, relevance scoring (from 1 to 100), and writing this data back to a custom field within your ATS. Projected hosting costs for systems processing around 1,000 applicants per month are typically under $25.
To enhance transparency for recruiters, the system would also generate a concise, 3-bullet summary explaining the rationale behind a candidate's score. This capability draws on Syntora's experience building similar document processing pipelines using the Claude API (for financial documents), where complex information is distilled into actionable insights. This summary would be directly accessible in your ATS, alongside the score, empowering recruiters with informed decision-making for faster human review.
A typical engagement for a system of this complexity, from discovery through deployment, would range from 8-12 weeks. Clients would need to provide access to their ATS and historical data. Key deliverables would include the deployed, integrated AI sourcing system, full source code, comprehensive documentation, and an operational handover, ensuring your team is fully equipped to utilize and maintain the solution.
What Are the Key Benefits?
Surface Top Candidates in 90 Seconds
The system screens, ranks, and summarizes a new applicant in under 90 seconds, freeing up hours of manual review each day for your team.
Pay Once, Own It Forever
A one-time project cost, not a recurring per-seat SaaS fee. After launch, you only pay for minimal AWS Lambda hosting costs.
Your Code, Your GitHub Repo
We deliver the complete Python source code and deployment scripts to your private GitHub repository. You are never locked into our service.
Alerts When Models Need a Refresh
We set up CloudWatch alarms that monitor result quality. You get a Slack notification if ranking performance degrades, signaling it is time to retrain.
Works Natively Inside Your ATS
Scores and summaries appear as custom fields in your existing ATS like Greenhouse or Lever. Your team's workflow does not change.
What Does the Process Look Like?
Week 1: ATS Connection & Data Audit
You provide read-only API keys to your ATS. We audit 12 months of historical data and deliver a Data Quality Report confirming you have enough successful placements to train a model.
Weeks 2-3: Model Build & Validation
We build the matching and ranking models using your historical data. You receive a Validation Report showing model accuracy on a holdout set of candidates.
Week 4: Production Deployment
We deploy the system on AWS Lambda and configure the ATS webhooks. You receive the deployed API endpoint and we test it with live applicant data.
Post-Launch: Monitoring & Handoff
For 30 days, we monitor the system daily. At the end of the period, you receive a full System Runbook for maintenance and future updates.
Frequently Asked Questions
- How much does a custom candidate sourcing system cost?
- The timeline is typically 4-6 weeks. Cost depends on the complexity of your ATS integration and the number of distinct roles you hire for. A firm hiring for 5 similar software roles is more straightforward than one hiring across sales, marketing, and engineering. We provide a fixed-price quote after our discovery call at cal.com/syntora/discover.
- What happens if the AI makes a mistake or the system goes down?
- If the AWS Lambda function fails, it automatically retries. If it still fails, it logs an error to CloudWatch and sends us an alert. The candidate is simply not scored, so your workflow is not blocked. We guarantee a 4-hour response time for any production issues during the first 90 days after launch.
- How is this different from using a tool like SeekOut or HireEZ?
- SeekOut and HireEZ are for outbound sourcing; they help find passive talent on external sites like LinkedIn. Our system is for inbound processing. It ranks candidates who have already applied to your jobs through your career page. It is built to save your internal team's time, not find new external leads.
- How do you handle potential bias in the AI model?
- We build bias-aware systems. During training, we explicitly exclude protected characteristics like name, age, and gender indicators from the model's features. The model ranks based on skills and experience. The final human review gate ensures a person always makes the ultimate decision, using the AI score as a guide, not a directive.
- What if we don't have much historical hiring data?
- We need at least 50 successful hires per major job category to train a reliable ranking model. If you have less data, we can build a semantic search system instead. It finds similar candidates based on a job description but does not provide a 1-100 score. This is still a major upgrade over keyword search.
- Do we need an engineering team to maintain this?
- No. The system is designed to run with minimal oversight, and we provide a runbook for basic checks. We also offer an optional monthly maintenance plan to handle all monitoring, updates, and retraining. The goal is for your recruiting team to use the system without thinking about the underlying technology.
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