DigitalHire

Overview

Recruiting teams at fast-growing companies spend the majority of their time on manual phone screens — a high-volume, repetitive task that delays hiring and limits how many roles a recruiter can run simultaneously. DigitalHire replaces that bottleneck with a fully automated AI recruiter pipeline. The platform sources candidates from LinkedIn, enriches contact data with verified emails and phone numbers, and initiates multi-channel outreach within 60 seconds of candidates being sourced. The voice screening layer runs up to 20 simultaneous AI conversations — real-time STT transcribes candidate responses as they speak, a conversational AI model manages dynamic dialogue and question branching, and TTS delivers the AI voice. Transcripts feed into an AI scoring engine that evaluates responses against recruiter-defined role scorecards, ranks candidates, and triggers auto-scheduling with no-show prediction. Since launch, DigitalHire has reached over 1 million candidates — cutting recruiting costs by 80% and reducing time-to-fill by 70% versus traditional screening processes.

Project Highlights

• Conversational AI voice pipeline — real-time STT, dialogue management, and TTS across 20 concurrent calls
• AI scoring engine evaluating transcribed responses against custom role scorecards
• LinkedIn sourcing pipeline with profile enrichment and contact verification
• Multi-channel outreach orchestration (voice, SMS, email) with <60s first contact
• No-show prediction and automated interview scheduling
• Async video interview module with AI evaluation and candidate ranking
• React.js web platform

ARCHITECTURE & APPROACH

Built as a sequential AI pipeline: a sourcing layer ingests and enriches LinkedIn profiles; a conversational voice layer handles up to 20 concurrent outbound calls using real-time speech-to-text, a conversational AI model for dynamic dialogue management, and text-to-speech for voice synthesis — with sub-500ms turn latency as a hard constraint; a scoring layer evaluates transcribed candidate responses against role-specific criteria and ranks candidates; a scheduling layer auto-books qualified candidates and manages reminders with no-show prediction. Each stage is decoupled so individual models and integrations can be improved independently without rebuilding adjacent layers.