AI applications
AI assistants and agents that answer from your own documents and data, talk to your customers by chat or phone, and connect to your tools. Built on OpenAI, the Claude API or Llama 3, with retrieval (RAG) through LlamaIndex and voice through Twilio.
What is it?
An AI application puts a large language model (LLM) to work on one clear job: answering customers, searching company documents, or taking phone calls. Retrieval-augmented generation (RAG) lets the model answer from your own data instead of only what it learned in training.
Why use it?
A general chatbot does not know your products, policies or records. An agent built on your own data gives answers you can check, and it can be connected to your phone line, website and internal tools.
How I build it
- Choose and configure the LLM for the job and the budget — OpenAI, the Claude API or Llama 3.
- Index your documents and data with LlamaIndex so the agent can retrieve them (RAG).
- Add voice with Twilio Programmable Voice, and speech-to-text with AWS Transcribe, where calls or voice notes are needed.
- Tune the prompts against real conversations.
- Serve it behind a FastAPI or Express API, deployed with Docker on AWS.
Why hire me?
I work as an OpenAI & Prompt Engineer at Xobot, and I built a no-code framework for creating custom AI agents with RAG and voice.