An AI voice platform that reads a job description, calls candidates, and returns a structured first-round assessment without a recruiter on the line.
High-volume hiring in India hits a wall at the first round: phone screening only scales as far as recruiters can dial in a day. Prep, scheduling, and uneven note-taking slow it further, and good candidates wait while the queue clears. The client wanted to screen at scale without dropping interview quality.
We designed and built an AI voice platform that handles the first round from start to finish. The core is a pipeline that reads a job description with generative AI, drafts role-specific objectives, and pairs them with a standard interview framework to produce an agent. Recruiters can edit that script before saving, so they keep the final say.
For the call, we wired up voice and telephony so the agent runs a real phone conversation, adapting to each answer as it works through experience, technical skills, notice period, and pay. Afterward, the system transcribes the call and writes a structured summary with ratings and insights, in the same format for every candidate.
Around that flow we added scheduling, reusable per-role agents, and a dashboard for activity, cost, and token usage. The build is modular, so later work like CV analysis, candidate ranking, and ATS integration has a clear place to slot in.
The interviewer, scheduler, and assessment engine were kept as separate parts. That made the build easier to manage and left obvious room for CV analysis, ranking, and ATS work later.
Speech and telephony sat with services built for them. VAPI, Deepgram, and Twilio carried those layers, and our team stayed on the interview logic and the recruiter experience.
Editable scripts and a single dashboard kept the automation transparent. Recruiters could shape each agent and see what was happening across their screening.
The agent was scoped to one task: run a structured screening interview and report on it. A narrow goal kept the output consistent and useful for real hiring decisions.
With one agent and one summary format per role, the process itself produced comparable results, whoever set it up.
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