The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
ā¢ 15h
I BUILT AN AI TRAVEL AGENT THAT PLANS AN ENTIRE TRIP IN MINUTES āļøš¤
Planning a trip usually means opening 10 different tabs.
ā Searching for flights
ā Comparing hotels
ā Looking for activities
ā Saving links
ā Organizing everything manually
So I built an AI Voice Agent using ElevenLabs + n8n that does it all through a simple conversation.
Here's how it works:
šµ The AI asks where you're traveling from and your destination.
šµ It collects your travel dates and number of travelers.
šµ It asks about your preferred activities.
šµ It generates a complete personalized travel itinerary.
šµ It emails everything directly to you.
The final result includes:
ā
Flight recommendations
ā
Resort and hotel options
ā
Direct booking links
ā
Personalized activity recommendations
ā
A complete travel plan delivered automatically
The exciting part isn't just that it answers questions.
It's that the AI understands the conversation, gathers the right information, and turns it into a personalized travel experience without any manual research.
This is where AI agents are heading.
Not just answering questions...
But completing real tasks from start to finish.
What business process would you trust an AI agent to handle for you?
AI AutomationAI Voice DevelopmentBusiness Workflow AutomationElevenLabsN8NOpenAIaiautomationvoiceaiaireceptionist
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Built a voice-first AI app in @usewonder.
The goal was simple: create something that makes people instantly think, "Damn, this feels like a real AI product."
I started with a few foundation screens, imported them into Wonder, then used Wonder Chat to expand the experience, refine the UX, and iterate much faster than I normally would.
My favorite feature has to be Shaders. It instantly made the interface feel more alive and premium. I also experimented with MCP + Codex to push the prototype further. Still learning that workflow, but it was fun to explore.
Here's a quick walkthrough of the project and how Wonder fit into my design process.
P.S. Ignore my grammar and occasional stuttering in the video. My brain renders thoughts faster than my mouth ships them. š
Meet AI is a video conferencing SaaS where AI agents join live meetings as active participants and respond in real time. I built the full application stack.
The hard part is the real-time layer. The backend provisions an AI agent the moment a meeting starts and keeps it running for the full call. Speech runs through a transcript pipeline and generates responses through the OpenAI API in near real time, so the agent replies while the conversation is still moving, not minutes later. After the meeting, the backend writes a structured summary through the same API, handled async by Inngest so nothing blocks the live app.
The stack is Next.js 15 and React 19 with the Stream Video and Stream Chat SDKs, tRPC and Drizzle ORM on Neon PostgreSQL, and Better Auth across the stack.
The platform runs 30 simultaneous live sessions, each with its own active AI agent, with no backend slowdown.
What the client gets: live AI agents that take part in real meetings, plus automatic summaries, on a stack built to hold many sessions at once.
Perfect
Echo is a multi-tenant SaaS for AI customer support with realtime chat, an AI voice agent, and AI automation. I was the primary developer on the full build, frontend, backend, and the AI layer. The AI is the core. It runs OpenAI, Claude, Gemini, and Grok through one multi-model setup, so a client can switch providers without a rewrite. A RAG pipeline connected to a vector database grounds every answer in the client's own content, so the chatbot never returns generic output. VAPI powers the voice agent, so customers can speak to support on a live call. Each tenant gets its own AI agent built on its own documents. The stack is Next.js 15 and React 19 inside a Turborepo monorepo, with separate apps for the dashboard, the embeddable chat widget, and backend services. Realtime chat runs on Convex. Clerk handles auth. API keys are encrypted per tenant through AWS Secrets Manager, so no two clients share credentials or data. Launch day held 60 live conversations at once with zero dropped sessions. What the client gets: an AI chatbot and voice agent that answer from their own content, work across multiple LLM providers, and stay isolated and secure per tenant.:
Perfect
Trending
Claude
Claude has entered the design space. How are you using Claude Design?
Contra University
Learn from expert creatives how to earn more using next-gen AI tools.
fifaworldcup2026
The World Cup is here and the whole world's watching. How are you designing for the world stage?
creativeaiflow
Creative AI workflows are evolving. What tools do you use, and what are their strengths and weaknesses?
freelancerlife
Freelancer life is wins, pivots, and everything in between. Whatās yours right now?