Build AI Recruiting Automation That Plugs Into Your HR Stack

Recruiting teams often hit a wall with their existing HR software's filtering capabilities. Most ATS platforms rely on simple keyword matching. A search for "Python" will miss a great candidate whose resume says "FastAPI expert" because the system cannot infer the relationship between the two skills. This rigid logic surfaces unqualified candidates and hides qualified ones.

A common next step is trying to connect the ATS to an AI tool with a no-code platform. The central challenge is parsing resumes, which are unstructured PDFs. A no-code workflow can trigger when a new resume is added, but its PDF parsing block fails to extract structured data like "years of experience with AWS" from a two-column resume layout. The workflow breaks down before the AI even sees the data.

Off-the-shelf AI recruiting platforms solve the parsing problem but introduce new ones. They are often full-suite replacements that force you to abandon your existing ATS and change your entire workflow. For a small firm that just needs to solve the initial screening problem, migrating their entire operation to a new platform is too disruptive and expensive.

Syntora's approach to integrating custom AI with your HR software would typically begin with a discovery phase to understand your specific workflow and the technical architecture of your existing Applicant Tracking System. The first step in building a resume screening system involves establishing a robust connection to your ATS. This would commonly be achieved by developing a Python service utilizing libraries like httpx to retrieve new candidate applications and their associated resume files as they are submitted. To ensure data integrity and provide a complete audit trail, every transaction – from receiving a resume to generating its final score – would be logged in a Supabase Postgres database.

For extracting structured data from resumes, the system would leverage the Claude 3 Sonnet API. Syntora would craft a highly specific prompt, instructing the large language model to emulate an expert technical recruiter, identifying and extracting a comprehensive set of features relevant to your hiring needs. This could include specific programming languages, years of experience with cloud platforms, and management history, producing a structured JSON output that offers greater reliability than basic text parsing. This parallels our experience building document processing pipelines using Claude API for sensitive financial documents, where accuracy and structured output are paramount.

Following data extraction, a custom ranking algorithm, typically implemented as a Python script, would score the identified features against the ideal profile for the open role. For example, a candidate matching 8 out of 10 required skills might receive a score of 80. The core processing, from resume receipt to score generation, is designed to be highly efficient, usually completing within seconds.

The entire service would be packaged in a Docker container for consistent deployment and then deployed as an AWS Lambda function. Syntora would configure a webhook within your ATS to trigger this Lambda function upon the arrival of each new applicant. Within seconds of processing, the function would write the computed score and a concise summary of the candidate's strengths back into a designated custom field on the candidate's profile within your ATS. This allows recruiters to access crucial insights directly within their familiar software environment without needing to navigate external tools. Syntora aims for a typical project timeline of 6-10 weeks for such a system, with the client providing access to their ATS documentation, example resumes, and actively participating in defining desired candidate features. The deliverables would include the deployed, production-ready AI integration, full source code, and comprehensive documentation.