Élevé Curates
How I Built an AI-Powered Workflow Automation Platform Using RAG and Autonomous Agents
Transforming luxury event planning through intelligent automation
Project Overview
Élevé Curates is a luxury event planning platform that leverages cutting-edge AI technology to automate time-intensive workflows. By implementing RAG-based conversational intake, autonomous agent systems, and intelligent vendor matching, I reduced planning time by 75% while maintaining the white-glove service expected by luxury clients. Built with modern cloud infrastructure and deployed on Vercel's Edge Network with AWS Route 53 for enterprise-grade reliability.
The Challenge
Manual Client Intake
Traditional client consultations took 2-3 hours, requiring extensive note-taking and follow-up questionnaires.
Time-Intensive Vendor Research
Researching and vetting vendors consumed 15-20 hours per event, with manual scoring and comparison.
Error-Prone Budget Tracking
Manual spreadsheet management led to budget overruns and missed expense tracking opportunities.
AI-Powered Solutions
Intelligent Client Intake
Conversational AI using RAG to capture requirements in 15 minutes
Smart Vendor Matching
AI agent autonomously scores and ranks vendors
Automated Workflows
12 autonomous workflows orchestrated via n8n
Predictive Budget Tracking
Real-time analysis with historical data insights
Technical Architecture
Frontend Layer
AI Layer
Backend Layer
Automation Layer
Deployment Layer
DNS Layer
Deployment & Infrastructure
GitHub Push
Code pushed to main branch
Vercel Auto-Build
Automated build & optimization
Edge Deployment
Deploy to 20+ global locations
DNS via Route 53
Enterprise DNS routing
Infrastructure Highlights
Key Innovations
Multi-Turn Conversational AI
Context-aware dialogue system that remembers preferences across sessions, reducing intake time from hours to minutes.
Context-Aware Recommendations
RAG-powered vendor matching that considers event type, budget, location, and historical performance data.
Autonomous Timeline Generation
AI-generated event timelines that adapt based on venue constraints, vendor availability, and best practices.
Results & Impact
Operational Efficiency
- 75% reduction in manual time
- 10x faster onboarding
- 85% tasks automated
Cost Optimization
- 87% infrastructure savings
- $20/mo Vercel deployment
- $5/month AI costs
Quality Improvements
- 95% AI accuracy
- 40% fewer budget overruns
- Better vendor matching
Performance
- Sub-100ms API response
- 95/100 Lighthouse score
- 20+ edge locations
Technologies Used
Infrastructure Cost Analysis
Before (AWS)
After (Modern Stack)
Key Learnings
RAG Quality Depends on Data
The accuracy of RAG-based recommendations is directly tied to the quality and structure of the vector database. Investing time in data curation and embedding optimization yielded significant improvements in AI accuracy.
Autonomous Agents Need Oversight
While AI agents can handle routine tasks autonomously, implementing human-in-the-loop checkpoints for critical decisions (like vendor contracts) maintains quality and builds client trust.
Vercel's Native Next.js Support Matters
Vercel's deep integration with Next.js 14 eliminated deployment complexity. Features like automatic edge optimization and instant rollbacks proved invaluable for rapid iteration.
DNS Separation Provides Flexibility
Using AWS Route 53 for DNS while deploying on Vercel created architectural flexibility. This separation allows platform changes without domain migration complexity.
Want to see it in action?
Hosted on Vercel Edge Network with AWS Route 53