Autonomous Hiring Ecosystem with Vector Search & Voice AI

StyxFlow

Building Styxflow was a deep dive into architecting scalable, event-driven AI systems. I gained critical experience in managing real-time, low-latency voice interactions using VAPI, ensuring the AI interview felt natural and conversational rather than robotic. Implementing Qdrant for vector storage taught me advanced semantic search techniques, allowing the system to match candidates based on context and capability rather than just keywords. Additionally, using BullMQ to decouple resume uploads from the main server reinforced the importance of asynchronous processing, ensuring the application remained performant and non-blocking during high-volume data ingestion.

Styxflow is an intelligent recruitment ecosystem designed to streamline the hiring process by bridging the gap between talent and recruiters through advanced AI automation. The platform serves a dual purpose: it empowers job seekers to showcase their actual competency beyond paper credentials and enables employers to make data-driven hiring decisions instantly. Upon registration, job seekers undergo automated video interviews generated dynamically based on their resume content. This ensures candidates are vetted on relevant skills immediately, scaling the screening process without human intervention. Employers post job descriptions, and the system utilizes an intelligent matching engine to rank candidates. It combines resume relevance with interview performance scores to suggest only the top-tier talent. o ensure transparency, employers can access detailed candidate profiles that include the full interview video recording, a transcript of the AI’s questions with the user’s responses, and an objective, AI-generated performance feedback summary.

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