Automate Candidate Sourcing with Custom AI Systems

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.

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.