Quick Summary
- ▸ Built for a healthcare startup in Panama City, Panama.
- ▸ CodeBranch helped a healthcare startup transform a manual, prompt-based prototype into a scalable, production-ready AI platform designed to assist physicians in real-time clinical settings.
- Full MVP delivered ahead of schedule, with additional functionality beyond the original scope.
- Scalable and maintainable production environment with CI/CD pipeline and code quality gates.
Overview
CodeBranch partnered with a healthcare startup to transform a proof-of-concept clinical assistant into a production-ready AI platform. The original prototype relied on a single, manually crafted prompt — effective for demonstrations but unsuitable for real-world clinical use. CodeBranch designed a multi-node LLM agent architecture using LangGraph, built a mobile-friendly web application with Next.js, and deployed the platform on a scalable infrastructure with CI/CD pipelines and automated code quality gates. The platform supports live consultation assistance, on-demand clinical chat, and an academic reference mode — all designed for physicians working in high-pressure emergency care environments.
Industries
Services Provided
Approach
The project was delivered by a six-person team: a Full Stack Developer, an LLM Engineer, a Project Manager, a Software Architect, and two QA Analysts. The backend was built with Python and FastAPI following Clean Architecture, with LangGraph orchestrating a multi-node LLM agent capable of routing across OpenAI, Anthropic, and AWS Bedrock. PostgreSQL with pgvector handled data persistence and semantic search. The frontend used Next.js with TypeScript, Tailwind CSS v4, NextAuth.js for authentication, and TanStack Query for efficient data fetching. Docker containers, a CI/CD pipeline, and SonarCloud integration ensured code quality and reliable deployments from day one.
From Prototype to Production-Ready Clinical AI
The client had validated their core idea with a proof-of-concept: a clinical assistant powered by a large, manually crafted prompt that could help physicians in emergency care settings. But moving from demo to product required a fundamentally different architecture — one that could handle multiple clinical scenarios, scale reliably, and meet the quality standards expected in healthcare.
CodeBranch assembled a six-person team to design and build the platform from the ground up. The backend was architected with Python and FastAPI following Clean Architecture principles, ensuring clear separation of concerns and maintainability. At the core of the AI capabilities, a multi-node LLM agent built with LangGraph replaced the monolithic prompt, enabling sophisticated clinical reasoning across multiple scenarios.
The platform supports three clinical modes:
- Live Consultation Assistance — real-time AI support during active patient encounters
- On-Demand Clinical Chat — quick medical queries answered with contextual accuracy
- Academic Reference Mode — evidence-based lookups for clinical decision support
PostgreSQL with pgvector provides both traditional data persistence and semantic search capabilities, allowing the platform to retrieve relevant medical context efficiently. The agent architecture supports multiple LLM providers — OpenAI, Anthropic, and AWS Bedrock — giving the platform flexibility to route queries to the best model for each task.
The frontend was built as a mobile-friendly web application with Next.js and TypeScript, designed for physicians who need to access the platform from any device in a clinical setting. NextAuth.js handles secure authentication, TanStack Query manages data fetching and caching, and Tailwind CSS v4 provides a clean, responsive interface.
A complete CI/CD pipeline with Docker and SonarCloud integration was established from day one, ensuring every commit was tested, analyzed for quality, and deployed automatically.
The result: a full MVP delivered ahead of schedule, with additional features beyond the original scope, a scalable production environment, and a platform ready for its debut at an international medical congress.
Results
- Full MVP delivered ahead of schedule, with additional functionality beyond the original scope.
- Scalable and maintainable production environment with CI/CD pipeline and code quality gates.
- Internal beta testing underway with cross-functional medical teams.
- Platform ready for showcase at an international medical congress.
