Build Custom Voice AI for Your HR and Recruiting Workflow

AI-powered ATS platforms promise voice analysis, but their models are generic. They score candidates on 'confidence' metrics trained on other companies' data, not the specific competencies your team hires for. You pay a high per-seat fee for a black-box model you cannot tune or inspect, and it often misses the nuance of your roles.

A more common approach is trying to connect a call recording tool to a transcription service like Otter.ai using Zapier. The problem is that Zapier only moves the raw text. It cannot perform analysis or apply conditional logic. A recruiter still has to read a 25-minute transcript to find the two minutes where a candidate discussed their AWS experience. This workflow costs hundreds in Zapier tasks and saves zero time.

One 20-person tech recruiting firm tried this. They used an ATS and conducted 50 phone screens per week. They zapped Aircall recordings to a transcription service and then into an ATS note. Recruiters said reading the wall of text was more work than listening to the recording at 2x speed. The $150/month Zapier bill added cost without solving the core problem.

Syntora would approach the development of a voice AI system for HR by first conducting a thorough discovery phase. This phase would involve auditing your existing call recording systems and ATS, such as Greenhouse or Lever. We would work with your team to pull a representative sample of historical interview recordings and their corresponding hiring outcomes. This data is crucial for defining the precise skills, keywords, and competencies the analysis model needs to identify for your specific roles and organizational culture. We find the Claude API particularly effective for its extensive context window and reliable instruction-following capabilities, which are critical for nuanced interview analysis.

The architectural core of such a system would be a Python application built with FastAPI. When a new interview recording is saved to your system, a webhook would trigger an AWS Lambda function. This function would securely stream the audio to the Claude API for transcription and subsequent analysis. Our engineers would craft a structured prompt to instruct the language model to extract specific entities relevant to your hiring criteria. This can include identifying years of experience with particular technologies, mentions of required certifications, and direct answers to your standard screening questions. While processing times vary, we design these systems to return results efficiently, often completing analysis for a typical 20-minute interview in under a minute.

The structured JSON output from Claude would then be processed by the FastAPI service. This service would calculate a relevance score based on criteria defined during discovery and format a concise summary. This summary, containing extracted skills and key quotes, would be posted directly to the candidate's profile in your ATS via its API. For data integrity and auditing, we would implement a Supabase database to log every transaction and cache results to prevent redundant processing.

Deployment of the system would typically use AWS Lambda for serverless execution, offering cost-effective scalability. We prioritize transparent operations through structured logging with `structlog`, tracking every step of the process. In cases where the Claude API returns an error or a transcription fails, a notification system would be configured, such as sending an alert to a Slack channel with the recording ID for manual review, allowing for high operational reliability. The goal is an operational uptime in excess of 99%.