HireVision AI

End-to-end AI hiring automation—built solo —powered by Raindrop, Vultr, ElevenLabs, and Gemini.

Comment 3

Inspiration

The Hiring Challenge Hiring is one of the biggest challenges for early-stage startups. As a solo developer and early-stage founder, I’ve experienced firsthand how costly and time-consuming the process can be. Screening resumes, coordinating schedules, and conducting repetitive first-round interviews can easily consume 40+ hours per hire—time that should be spent building and improving the product. This friction slows execution, drains momentum, and disproportionately impacts small teams.

Large enterprises solve this problem with dedicated HR departments, automation, and expensive recruiting platforms. Small startups, by contrast, are often limited to manual processes and basic tools like spreadsheets.

Introducing HireVision AI

HireVision AI was born from a simple but powerful question: What if a two-person startup could hire with the same speed, intelligence, and efficiency as a company like Google?

That question became the foundation of this project. The goal was to build a fully autonomous hiring assistant—one capable of handling resume screening, conducting interview conversations, generating actionable analytics, and supporting intelligent hiring decisions, all without requiring a human recruiter. HireVision AI is designed to level the playing field, enabling small teams to hire faster, smarter, and with confidence.

What it does

HireVision AI is an end-to-end, AI-powered hiring platform, designed and built entirely solo in just 15 days for the AI Championship 2025. The system automates the entire first half of the hiring pipeline, enabling companies to screen, assess, and rank candidates with minimal human involvement.

✨ Core Capabilities

Autonomous AI Voice Interviews Conducts structured, role-specific interviews without human recruiters.

Real-Time Proctoring & Integrity Monitoring Face detection, sentiment analysis, and attention tracking ensure interview authenticity.

Intelligent Resume Evaluation & Scoring Analyzes resumes against job requirements using AI-driven matching.

Automated Candidate Ranking Generates data-backed shortlists based on performance, skills, and behavioral signals.

Real-Time Analytics Dashboard Provides actionable insights for faster, more informed hiring decisions.

Secure Authentication & Role-Based Access Control Enterprise-grade security with granular user permissions.

Architecture & Impact

Despite its simplicity on the surface, HireVision AI is powered by a robust, scalable architecture. Under the hood, it integrates Raindrop, Vultr, ElevenLabs, and Gemini, orchestrated through a production-grade Next.js application to deliver real-time performance, reliability, and intelligent decision-making.

How I built it

This project was built end-to-end, entirely solo, covering every layer of the stack:

  • UI/UX design and frontend implementation
  • Raindrop MCP integration
  • Vultr database and object storage setup
  • Real-time face detection and proctoring logic
  • Firebase Authentication and WorkOS configuration
  • ElevenLabs real-time streaming voice integration
  • API routes, pipelines, and robust error handling
  • Resume–job matching logic and analytics systems
  • Demo scripting, recording, and post-production

This was one of the most intense yet rewarding builds I’ve undertaken—marked by rapid learning, late-night debugging, and carefully stitching multiple systems together until everything worked seamlessly.

🧠 How HireVision AI Uses Raindrop

At the core of HireVision AI sits Raindrop, which functions as the intelligence layer of the entire platform:

  • SmartSQL – Enables AI-driven PostgreSQL queries without complex backend logic
  • SmartMemory – Preserves conversational and interview context across sessions
  • SmartInference – Processes resumes and computes structured match scores
  • SmartBuckets – Handles secure file storage and controlled access

Raindrop made it possible to design advanced reasoning workflows quickly and cleanly, without writing excessive backend code. It is quite literally the brain of HireVision AI.

🏗️ How HireVision AI Uses Vultr

Vultr powers the platform’s backend infrastructure, providing production-grade reliability and scalability:

  • Vultr Managed PostgreSQL stores job postings, candidate data, evaluations, and analytics
  • Vultr Object Storage manages uploaded resumes and supporting documents
  • Low-latency global performance ensures smooth interview execution and real-time scoring

Vultr enabled the system to move beyond a demo environment and operate as a scalable, production-ready platform with minimal operational overhead.

🔊 ElevenLabs + Gemini Integration

  • ElevenLabs delivers real-time, natural-sounding AI voice interviews
  • Gemini 2.0 Flash handles reasoning, adaptive follow-up questions, sentiment analysis, and structured outputs

Together, they create a highly realistic interview experience—responsive, conversational, and context-aware—closely mimicking a human recruiter.

Challenges I ran into

Multi-service orchestration Integrating Raindrop, Vultr, ElevenLabs, Gemini, Firebase, and WorkOS while maintaining a fast, responsive UI required careful architectural decisions.

Real-time voice interview logic Streaming audio, generating intelligent follow-ups, and synchronizing analytics in real time was one of the most technically complex parts of the system.

Intelligent resume scoring Balancing speed with meaningful, explainable scoring required multiple design iterations.

Solo development under time constraints Handling frontend, backend, AI pipelines, infrastructure, and demo production alone within 15 days pushed both technical and personal limits.

🏆 Accomplishments I’m Proud Of

Despite the challenges, HireVision AI reached a level of polish and functionality that exceeds typical hackathon projects—especially given the solo build timeline.

Fully functional end-to-end hiring automation From resume upload to AI interview to analytics, the entire pipeline operates seamlessly in real time.

Real-time AI interviews with voice, sentiment, and proctoring The interviewer feels natural and intelligent, powered by deep integration between ElevenLabs, Gemini, and Raindrop.

Smart resume analysis with 85%+ reliable scoring accuracy Generates structured insights, skill alignment, and candidate rankings with strong consistency.

Production-ready backend architecture Built on Vultr Managed PostgreSQL, Object Storage, and secure pipelines—ready to scale beyond demo usage.

Clean, intuitive UI built from scratch Even under extreme time pressure, usability and clarity were never compromised.

A truly solo-built system Every line of code, architectural decision, integration, and demo asset was created by one person.

Enterprise-grade user experience The seamless interaction between AI systems, backend infrastructure, and frontend design makes HireVision AI feel like a real production product—not a prototype.

What I learned

  • How to design and scale multi-agent systems
  • How to integrate Raindrop Smart components effectively
  • Real-time audio + sentiment processing
  • Better system design, caching, and reliability patterns
  • How to manage large projects solo under pressure

Most importantly, I learned how powerful small teams (or even one person) can be when backed by great tools.

🚀 What’s Next for HireVision AI

HireVision AI began as a 15-day solo build, but it has already demonstrated the potential to evolve into a global, full-scale hiring platform purpose-built for startups and growing teams. The next phase focuses on expanding autonomy, collaboration, intelligence, and scale.

1. Multi-Agent Hiring Workflow Introduce specialized AI agents to manage each stage of hiring:

  • Job description refinement
  • Automated candidate sourcing and outreach
  • AI-driven shortlisting and screening
  • Interview summarization and insights
  • Offer letter generation

This enables a fully autonomous, end-to-end hiring pipeline.

2. Team Collaboration & Decision-Making Enable hiring teams to:

  • Review candidates collaboratively
  • Leave structured comments and evaluations
  • Share AI-generated insights
  • Approve or reject candidates together

Bringing startup hiring workflows closer to enterprise-grade HR systems.

3. Advanced Proctoring & Behavioral Intelligence Enhance interview analysis with:

  • Emotion timeline visualization
  • Gaze and attention consistency tracking
  • Attention drift detection
  • AI-scored soft-skill and behavioral evaluation

Helping teams make more accurate, bias-aware hiring decisions.

4. Employer & Candidate Mobile Apps Launch lightweight mobile applications for:

  • On-the-go AI interviews
  • Instant application status updates
  • Push notifications for hiring teams
  • Real-time hiring dashboards

Improving accessibility and speed across devices.

5. Auto-Sourcing & AI Outreach Enable HireVision to:

  • Discover candidates across the web
  • Automatically evaluate public profiles
  • Send personalized, AI-generated outreach messages

Reducing sourcing time from weeks to hours.

6. Marketplace for Voice Interview Models Create an Interview Voice Marketplace where:

  • Employers select interviewer personalities
  • Domain-specific interview models are available
  • Candidates experience more tailored, role-specific interviews

Making interviews both customizable and scalable.

7. Enterprise-Ready Capabilities Add support for:

  • Single Sign-On (SSO) integrations
  • Audit logs and hiring traceability
  • Applicant tracking pipelines
  • Compliance and data privacy controls

Allowing HireVision to scale beyond startups into larger organizations.

8. Monetization & Startup-Friendly Pricing Introduce flexible pricing models:

  • Pay-per-interview
  • Subscription plans
  • Usage-based AI credits
  • Team-based pricing tiers

Designed to stay affordable for small teams while scaling sustainably.

9. AI-Curated Candidate Profiles Automatically generate comprehensive candidate dossiers:

  • Skill maps and experience summaries
  • Culture-fit analysis
  • Project and impact breakdowns
  • Strengths and improvement areas

Delivering a complete candidate profile in seconds.

10. Global Scaling with Vultr Leverage global infrastructure to:

  • Deploy regionally
  • Reduce interview latency
  • Support international users
  • Deliver smooth real-time experiences worldwide

The Long-Term Vision

HireVision AI becomes the default hiring system for startups—an autonomous, intelligent recruiter that scales globally while remaining simple, fast, and accessible.

Built With

  • elevenlabs-api
  • firebase-auth
  • google-gemini-flash
  • next.js
  • node.js
  • raindrop-mcp
  • stripe
  • tailwind-css
  • typescript
  • vultr-managed-postgres
  • vultr-object-storage
  • webrtc
Share this project:

Updates

posted an update —

HireVision AI is Live!

After long time of intense coding, coffee, and debugging, I am thrilled to share HireVision AI with the community!

I built this solo to prove that small teams can have enterprise-grade hiring tools. I'd love to hear your feedback in the comments!

Log in or sign up for Devpost to join the conversation.

posted an update —

Inspiration Small startups struggle with hiring more than anything else. As a solo developer and early-stage builder, I’ve personally experienced how painful and time-consuming the hiring process can be. Screening resumes, scheduling calls, conducting repeated first-round interviews — it can easily take 40+ hours per hire, which slows down product development and kills momentum. Large enterprises have automation, full HR teams, and expensive tools. Small teams have… Google Sheets. HireVision AI was inspired by one simple question: “What if a 2-person startup could hire with the same power and speed as Google?” That question became the foundation of this project. I wanted to create a fully autonomous hiring assistant — one that handles resume screening, interview conversations, analytics, and intelligent decision-making, all without needing a human recruiter.

What it does HireVision AI is a complete AI hiring system built fully solo in 15 days for the AI Championship 2025. The system automates the entire first half of the hiring pipeline: Core Capabilities Autonomous AI voice interviews Real-time proctoring (face detection, sentiment, attention tracking) Smart resume evaluation and scoring Automated candidate ranking Real-time dashboard analytics Secure authentication & role-based access The platform feels simple for the user, but underneath it runs a powerful combination of Raindrop + Vultr + ElevenLabs + Gemini, connected through a production-grade Next.js application.

How we built it How It Uses Raindrop I built this project end-to-end: Designed the UI/UX Integrated Raindrop MCP Connected Vultr database + storage Implemented real-time face detection Configured Firebase Auth + WorkOS Integrated ElevenLabs streaming voice Built API routes, error handling, and pipelines Wrote matching logic and analytics Recorded, edited, and scripted the demo This was one of the most intense but rewarding builds of my life — learning nonstop, debugging late nights, and stitching systems together until everything clicked. Raindrop is the intelligence layer of the entire system: SmartSQL → AI-driven PostgreSQL queries SmartMemory → Stores conversational context across interviews SmartInference → Processes resumes and calculates match scores SmartBuckets → Secure file storage & access Raindrop allowed me to build advanced reasoning workflows without writing huge amounts of backend code. It is the “brain” of HireVision AI.

How It Uses Vultr Vultr powers the backend infrastructure: Vultr Managed PostgreSQL stores all job postings, candidates, evaluations, and analytics Vultr Object Storage handles uploaded resumes and associated documents Fast global performance ensures the app runs smoothly during interviews and scoring Vultr gave the project scalability and production reliability with minimal setup.

ElevenLabs + Gemini ElevenLabs provides the real-time, conversational AI voice that conducts interviews. Gemini 2.0 Flash powers the reasoning, follow-up questions, sentiment understanding, and structured outputs. Together, they create a surprisingly natural interview experience.

Challenges we ran into Multi-service integration Raindrop, Vultr, ElevenLabs, Gemini, Firebase — combining all these while keeping the UI fast required careful design. Real-time voice interview logic Streaming audio, generating follow-ups, and syncing analytics was complex. Smart resume scoring Designing a system that feels intelligent but also fast required experimentation. Building everything solo From backend to frontend to AI pipelines to video demo — doing it all alone in 15 days pushed me to my limits. ## Accomplishments that we're proud of Despite the challenges, HireVision AI reached a level of polish and functionality I’m truly proud of — especially as a solo build completed in just 15 days. Fully functional, end-to-end hiring automation From resume upload to AI interview to analytics — everything works seamlessly in real time. Real-time AI interviews with voice, sentiment, and proctoring The interviewer feels natural, responsive, and intelligent, thanks to ElevenLabs + Gemini + Raindrop integration. Smart resume analysis with 85%+ reliable scoring The system generates structured insights, skill matches, and ranking with impressive consistency. Production-ready backend with Vultr Managed PostgreSQL + Object Storage + secure pipelines mean the platform can scale beyond demo usage. Clean, intuitive UI built from scratch Even under time pressure, the interface remained simple, elegant, and easy for users to navigate. A fully solo-built project Every line of code, every design decision, every integration, and the entire demo was made by one person — which makes the final result even more meaningful. A unified experience that feels enterprise-grade The smooth flow between AI systems, backend infrastructure, and UI elements makes the app feel like a real production product, not a hackathon prototype. What we learned How to design and scale multi-agent systems How to integrate Raindrop Smart components effectively Real-time audio + sentiment processing Better system design, caching, and reliability patterns How to manage large projects solo under pressure Most importantly, I learned how powerful small teams (or even one person) can be when backed by great tools.

What's next for HireVision HireVision AI began as a 15-day solo project, but it has already shown the potential to grow into a full-scale hiring platform for startups around the world. Here’s what comes next:

  1. Multi-Agent Hiring Workflow Introduce specialized AI agents that handle: Job description refinement Candidate outreach automation AI-driven shortlisting Interview summary generation Offer letter automation A complete, autonomous hiring pipeline.

  2. Team Collaboration Features Allow multiple team members to: Review candidates together Leave comments and evaluations Share AI-generated insights Approve hiring decisions Bringing startup workflows closer to enterprise HR systems.

  3. Advanced Proctoring & Behavioral Insights Enhance the interview system with: Emotion timeline analysis Gaze consistency tracking Attention drift detection AI-scored soft-skill evaluation Helping teams identify the strongest candidates more accurately.

  4. Employer & Candidate Mobile Apps A lightweight mobile version of HireVision for: On-the-go interviews Instant application status updates Push notifications for hiring teams Real-time dashboards

  5. Auto-Sourcing & AI Outreach Enable the system to: Find candidates across the web Evaluate profiles automatically Send personalized AI-written outreach messages Reducing sourcing time from weeks to hours.

  6. Marketplace for Voice Interview Models Create an “Interview Voice Store” where: Employers choose interviewer personality Domain-specific interview models exist Candidates experience more tailored interviews

  7. Enterprise Features Add support for: SSO integrations Audit logs Applicant tracking pipelines Compliance & data privacy controls Allowing HireVision to scale beyond startups.

  8. Monetization & Startup Plans Introduce: Pay-per-interview Subscription plan Usage-based Raindrop credits Team-based pricing Designed to keep the product affordable for small teams.

  9. AI-Curated Candidate Profiles Automatically generate: Skill maps Experience summaries Culture fit analysis Project breakdowns Strength & improvement areas A complete candidate dossier in seconds.

  10. Scaling globally with Vultr With Vultr’s global data centers: Deploy regionally Reduce latency Expand to international users Provide a smoother real-time experience

Log in or sign up for Devpost to join the conversation.