Retrieval-Augmented Generation (RAG) Conversational Agent
Built using Python and FastAPI, the backend leverages RESTful APIs and asynchronous processing to ensure high-speed data handling and seamless system performance. The architecture is optimized for scalability, security, and maintainability, making it suitable for high-volume processing environments.
A dedicated test UI was developed to validate system functionality, improve usability, and ensure a smooth user experience. The interface is structured with staff convenience in mind, enabling intuitive navigation, simplified data access, and efficient task management — ultimately improving productivity and reducing operational complexity.
👋 I'm new to Contra and I did my first post without realizing I hadn't even introduced myself!
Hey I'm Katya, I'm an AI Systems Architect, marketer and yoga teacher.
I build AI systems that bring in business and handle the work behind it. A voice agent that calls new leads back within seconds and books the appointment. A pipeline that traced 16,900+ leads to the ads that brought them. Listing videos made from the photos a real estate agent already has.
The marketing side came first. I built the multi-channel campaign for Jay Shetty's launch (22 million followers). After that I discovered the tech world and when AI came along, I realized I'm a builder at heart and never looked back! Building is so much fun 🤍
Last thing, if you're building with AI video or automation, I'd like to see what you're working on
And if you're hiring: I may be new to Contra but definitely not new to this work. Reach out for AI agent builds, apps, websites or anything in between.
P.s The video below is my AI avatar. My profile video is the real me :)
Then: 1 month, 1 template.
Now: 1 week, 15 templates.
Same designer, different workflow.
I wrote up how I built no-code.supply: fifteen website templates, each with its own brand, in HTML and React, and some in Framer too. Claude Code did most of the typing. I did the directing.
I built an AI agent that handles customer operations — refunds, order lookups, and support tickets — with a human approval gate built into the workflow. The agent proposes the action, pauses, and waits for a human to approve before anything executes. The LLM never makes the final call. Role-based access and a full audit trail are enforced in code, not prompts. Deployed live on Azure Container Apps.