This prototype was delivered through an approval-driven, phase-gated process. Select a gate to see what it covered.
Name the problem precisely and draw the lines around it.
Detect → Reach → Converse → Recover. A continuous loop that brings quiet candidates back without ever taking a hiring decision out of human hands.
A 5-minute sweep reads time-in-stage across the whole funnel and flags candidates going cold — computed in the prototype, since the ATS emits no native "cold" event.
A pre-approved, compliant WhatsApp template re-opens the conversation. The candidate’s reply opens a 24-hour window for free-form, adaptive chat.
Winston, the AI assistant, runs a warm, personalized, multi-turn conversation — gathering answers or booking time via in-chat WhatsApp Flows.
It logs the outcome back to the ATS and alerts the recruiter. It never auto-advances or rejects — every stage move stays human.
The items below are the production roadmap — a forward look, not current POC features. The prototype proves the workflow today; production turns the simulated surfaces into live, always-on ones.
Swap the simulated ATS and WhatsApp surfaces for live SmartRecruiters and WhatsApp Business connections — a binding swap behind the same workflow.
Move the detection sweep onto always-on cloud crons so re-engagement runs continuously, with no one pressing a button.
Extend beyond WhatsApp to SMS, email, and web chat, meeting each candidate on the channel they actually answer.
Layer in recovery-rate, time-to-reconnect, and cohort reporting so the business can see exactly what re-engagement returns.
The foundations page covers the public-domain research it was grounded in and the cloud infrastructure it runs on, with zero Sutherland data. The dashboard shows the live prototype in action.