Client AI Chat System
A custom AI chat system delivered for a client, including a web chat interface, backend API support, and a deployment path clear enough for review, testing, and iteration.
Role
Full-stack Engineering / AI Chat Product Build
Timeline
Client project
Status
Client work
Proof
Delivered custom AI chat interface with backend API support
What Needed To Change
The client needed a dedicated web chat experience for a specific use case, rather than a generic chat widget or simple UI prototype. The system needed a usable interface, backend support, a clear deployment structure, and room for future improvements after review. For AI chat products, the challenge is rarely only the interface. Real delivery needs to connect user flow, API/backend behavior, chat state, deployment, testing, and the constraints of the use case. At the same time, because this was client work, domain details, data, and business context needed to be protected when presented in the portfolio.
What We Built
- Built a custom web chat interface suited to the client's use case and conversation flow.
- Designed an application structure so the chat experience had backend/API support, not only a UI prototype.
- Connected the frontend to the necessary backend flow for sending messages, receiving responses, and handling chat state.
- Standardized layout, interaction, and basic states so the client could review the experience clearly.
- Prepared a deployment-ready build for demo, testing, and handoff.
- Anonymized sensitive information for the case study while preserving the technical proof without exposing client context.
Outcome
The project produced a real AI chat system for a client, with a web interface, backend/API support, and a clearer deployment path than a standalone prototype. The client could review the flow, test the experience, and continue iterating on a structured build. This case demonstrates Mikel Studio's ability to deliver client AI product work: not just building a chat interface, but handling full-stack application structure, backend integration, deployment readiness, and safe proof packaging when client information needs to stay private.
Stack
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