Automate Temp Staff Recruiting with a Custom Voice AI Agent

Many staffing agencies start with call center platforms or IVR systems. These tools fail because their rigid, menu-driven logic (“Press 1 for yes, Press 2 for no”) cannot handle the natural, open-ended questions of a candidate screen. A candidate who says “I can start next Monday” instead of “Yes” breaks the script, requiring manual follow-up.

A common workaround is using a tool like Zapier to connect an SMS service to a text-to-speech API. This approach creates a one-way notification, not a conversation. The latency is too high for a real-time call, and there is no state management. If a candidate asks about the pay rate, the workflow has no way to look up the answer and respond.

Consider an agency needing to screen 50 forklift operators for a weekend shift. A recruiter spends 4 hours making calls, leaving voicemails, and logging outcomes. The manual process is too slow to fill urgent temp roles. They need a system that can contact all 50 candidates in parallel and deliver a ranked shortlist within an hour.

Syntora's engagement would begin by collaboratively mapping your existing screening script into a structured state machine. This state machine would manage the conversation flow within a Python application. For understanding and responding to open-ended candidate answers, the system would use the Claude API. This approach allows the agent to manage conversational nuances while reliably gathering required data, such as license numbers or years of experience. We have extensive experience building similar document processing pipelines using the Claude API for financial documents, and the same patterns apply to candidate screening in this industry.

The core system would typically be architected as a FastAPI application, often deployed on serverless platforms like AWS Lambda for scalability and cost efficiency. The initial integration would involve configuring a webhook to trigger a Lambda function when a new candidate enters your ATS. This function would be responsible for initiating an outbound call, managing the conversation state, and orchestrating the AI's responses. The design ensures efficient processing of calls with clear response times, though specific durations would depend on script length and conversational complexity.

Following the call, the system would transcribe the entire conversation. A subsequent Claude API call would then extract structured data, including a concise summary and a qualification score. This extracted data would then be posted back to your Applicant Tracking System, supporting platforms like Bullhorn or JobDiva. The goal is for your recruiters to access a clear summary directly on the candidate's record, eliminating the need to listen to full recordings.

For storing configuration data, such as job-specific pay rates or screening questions, Syntora would integrate a flexible database like Supabase, enabling updates by non-technical staff. Logging would be managed using structlog, sending structured JSON to services like AWS CloudWatch. This detailed logging supports the creation of monitoring dashboards to track key operational metrics, including call completion rates and AI transcription error rates. We would provide an estimate for typical monthly hosting costs, which would vary based on call volume and specific cloud provider usage.