Stop Manually Reviewing Screening Calls. Use Voice AI.
The best voice AI tools for recruiting are custom systems that transcribe and summarize candidate screening calls. These systems extract key data like skills and salary expectations and sync it directly to your Applicant Tracking System (ATS).
A typical system ingests audio files, runs them through a transcription service, and uses an AI model like Claude to pull out structured information. The complexity depends on your ATS integration and the number of distinct roles you hire for. A firm hiring for three similar sales roles is simpler than one hiring for ten different engineering roles.
We built a voice analysis pipeline for a 12-person recruiting firm that processes 400 applicants per month. It cut their review time for each 20-minute screening call from 15 minutes down to 90 seconds. The entire system was designed and deployed in four weeks.
What Problem Does This Solve?
Recruiting platforms like Humanly.io or Paradox offer AI screening, but they are built for large enterprises. For a small firm, their five-figure annual contracts and per-seat pricing are not feasible. Their workflows are also rigid; you cannot easily customize the summary format or the specific data points extracted from a call.
Many teams then try to build a partial solution using a transcription API like AssemblyAI or Deepgram. This approach fails because transcription is only 10% of the work. You get a wall of text, but a recruiter still has to spend 10-15 minutes reading it to find the candidate's salary requirements, notice period, and specific technical proficiencies. The core bottleneck, manual data extraction and entry, remains unsolved.
A 7-person staffing agency tried this with AssemblyAI. They piped call transcripts into their ATS, Bullhorn, but their recruiters still spent hours manually reading each one to fill out custom fields. They paid for an API but saw no meaningful reduction in time-to-submit, because the high-value summarization work was still completely manual.
How Does It Work?
Our process starts by ingesting your call recordings, typically MP3 or WAV files from a VoIP system like Aircall, into an AWS S3 bucket. An S3 event trigger invokes an AWS Lambda function. This function first sends the audio to AWS Transcribe, configured for speaker diarization to separate the recruiter's and candidate's speech for cleaner analysis.
Once we have the raw transcript, the core logic runs in Python using the Claude API. We design a detailed prompt that instructs the model to act as a senior technical recruiter and extract specific data points into a structured JSON format. This includes fields like 'salary_expectations_usd', 'key_skills_mentioned', 'relocation_status', and a concise 150-word summary of the conversation. This step transforms an unstructured transcript into clean, usable data in about 25 seconds for a 15-minute call.
The resulting JSON object is then posted to your ATS. We write a custom integration using Python's httpx library to interact with the REST API of platforms like Bullhorn, Crelate, or Greenhouse. The data populates the candidate record automatically, typically attaching the summary as a note. The entire end-to-end process from audio upload to ATS update completes in under 45 seconds.
For monitoring, the FastAPI service uses structlog for structured JSON logs, which are sent to AWS CloudWatch. We configure alerts that fire on specific error codes or processing times exceeding 60 seconds. These alerts are sent to a dedicated Slack channel, allowing for immediate investigation. Most transient API errors are handled with a built-in exponential backoff retry mechanism.
What Are the Key Benefits?
Get Summaries in 45 Seconds, Not 15 Minutes
The entire pipeline from call recording to ATS entry is complete in under a minute. Your recruiters can review candidates almost instantly after a screening call ends.
One Fixed-Price Build, No Per-Seat Fees
We build and deliver the system for a single project fee. Your only ongoing cost is for cloud hosting, typically under $50 per month, not a recurring SaaS subscription.
You Own The Code and The System
We deliver the complete Python source code in your private GitHub repository. You are not locked into a vendor and can have any developer extend it in the future.
Proactive Monitoring for System Health
We configure CloudWatch alerts that notify you in Slack if an integration fails or the AI model returns an error. The system tells you when it has a problem.
Data Appears Natively In Your ATS
The system integrates directly with Bullhorn, Crelate, Greenhouse, and other platforms with a REST API. No new software for your recruiters to learn.
What Does the Process Look Like?
Workflow & ATS Audit (Week 1)
You provide access to your ATS and a sample of 10-20 call recordings. We map your exact data requirements. The deliverable is a technical spec outlining every field to be extracted.
Core System Build (Week 2)
We write the Python code for transcription, AI summarization, and ATS integration. The deliverable is a set of sample JSON outputs generated from your call recordings for you to approve.
Deployment & Live Testing (Week 3)
We deploy the system on AWS Lambda and connect it to your live ATS. The deliverable is a live, end-to-end demonstration where you see a call summary appear in a candidate record.
Monitoring & Handoff (Week 4)
We set up logging and alerting, then monitor the system for one week of live use. The final deliverable is the complete source code, deployment scripts, and a runbook for maintenance.
Frequently Asked Questions
- What does a custom voice AI system for recruiting typically cost?
- The cost depends on the complexity of your ATS integration and the number of distinct screening workflows you have. A project with a single role type and a modern ATS with a well-documented API is a standard 4-week build. Supporting multiple, very different roles or integrating with a legacy system can add time. We provide a fixed-price quote after our discovery call.
- What happens if a call transcription is inaccurate?
- Transcription is never perfect, especially with poor audio quality or strong accents. Our Claude API prompt is designed to handle minor inaccuracies. For critical fields like salary, the prompt instructs the model to return 'null' if the information is ambiguous. This flags the record for a quick manual review, preventing bad data from entering your ATS.
- How is this different from a conversation intelligence tool like Gong?
- Gong is designed for sales coaching and revenue intelligence. It analyzes call sentiment and sales rep performance. Our system is a purpose-built recruiting tool designed for one job: extracting structured candidate data from screening calls and loading it into an ATS. It is a lightweight automation pipeline, not a broad analytics platform.
- Does the system work with languages other than English?
- Yes. We use AWS Transcribe and the Claude API, both of which support dozens of languages. We would build a separate, language-specific prompt for each one you need to support. Adding another language like Spanish or French typically adds 2-3 days of development to the project scope to ensure the extraction quality is high.
- What if my recruiters don't follow a strict script?
- The system is designed for natural conversation, not a rigid script. It works better that way. The AI model is trained to understand context and intent from conversational language. We do provide a one-page best practices document, which suggests explicitly confirming key data points like salary, but it is a guideline, not a requirement.
- What is the ongoing maintenance like?
- The system is built to be low-maintenance. The primary ongoing cost is for AWS services, usually under $50/month at moderate volume. The system self-monitors for errors. We offer an optional flat monthly plan that covers prompt updates, dependency upgrades, and troubleshooting for any API changes from your ATS vendor.
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