How We Built an AI Agent in 48 Hours
A behind-the-scenes look at how we rapidly prototype AI agents using LangChain and Claude API.
At Zesxai, we believe in moving fast without breaking things. In this post, we share how our team built a fully functional AI agent in just 48 hours, from concept to deployment.
The Challenge: A client needed an AI assistant that could parse medical documents, extract key data points, and generate summaries. Traditional approaches would take weeks. We had a weekend.
Our Approach: Day 1 Morning - Requirements gathering and architecture design. Day 1 Afternoon - Core LLM pipeline with Claude API. Day 2 Morning - RAG pipeline with pgvector for document retrieval. Day 2 Afternoon - Testing, deployment, and handoff.
Key Takeaways: Start with the simplest possible architecture. Use managed services (Supabase, Vercel) to eliminate ops overhead. Test with real data from hour one. Ship early, iterate based on feedback.
The result? A production-ready AI agent that the client is still using today, processing hundreds of documents daily.
