Build Your Custom Voice AI Recruiter and Scheduler

Many SMBs first try SaaS platforms like Paradox or Talkpush. These tools are designed for enterprise scale and high-volume, low-skill hiring. Paradox forces a rigid, multi-stage implementation that is overkill for a small team, and its pricing is prohibitive. You end up paying for enterprise features you never use.

Talkpush is more accessible but fails on nuance. Its keyword-based scoring cannot distinguish between a software developer who has 'used Python' and one who has deployed production FastAPI applications on AWS. A 15-person firm using it to screen 80 applicants might get 30 keyword-matched 'successes'. A human recruiter then spends a full day listening to recordings, only to find 25 of them are unqualified, generating more manual work than it saved.

These tools fundamentally fail because they treat recruiting as a generic funnel. For an SMB, every hire is critical. You need screening logic that reflects the actual requirements of the job, not a system that just counts keywords. The unpredictable per-applicant pricing also makes budgeting impossible.

Syntora's engagement would begin with a discovery phase to audit your current recruiting workflow and identify precise screening criteria. This helps define the technical requirements for the voice AI system.

The system architecture would be designed to connect directly to your ATS API, supporting platforms like Greenhouse, Lever, or others. Syntora would develop a Python-based webhook listener using httpx that triggers the workflow when a candidate reaches a 'Phone Screen' stage in your ATS. Temporary storage for candidate transcripts and scheduling states would use Supabase for data reliability during processing.

Upon trigger, the system would initiate a call using a programmable voice API. A conversational agent, designed with the Claude API, would ask your specified role-specific screening questions. The call flow logic would be managed by a FastAPI service. Following the call, the full audio transcript would be processed to extract structured data points such as years of experience or salary expectations. We would aim for rapid processing of these transcripts to minimize delays in candidate progression.

If the candidate's responses meet predefined criteria, the agent would access your team's Google or Microsoft 365 calendars to identify and offer available interview slots. Once a time is confirmed, the system would send calendar invitations to all parties and automatically update the candidate's status in your ATS. This automated screening and scheduling process would be engineered for efficiency.

The system would be designed for deployment on serverless platforms such as AWS Lambda, which helps manage infrastructure costs for varying screening volumes. We would implement structured logging with structlog and configure alerts for any API failures. Deliverables would include the deployed system, source code, documentation, and a simple dashboard to monitor call volumes, pass rates, and scheduling success.

A typical build timeline for a system of this complexity, including discovery and deployment, is 6-10 weeks. Your team would need to provide access to ATS APIs, calendar APIs, and clearly defined screening questions and criteria.