AI & LLM
AI Appointment Assistant
We built a conversational AI assistant prototype for hospital appointment routing and service guidance using multi-agent workflows and LLM-based intent handling.
- Python
- LangGraph
- LangChain
- OpenAI API
Problem
Routing patients to the right department and a suitable appointment created a heavy, repetitive workload at the call center. A simple chatbot couldn't understand context or perform a real action.
Solution
We designed a multi-agent LLM workflow that handles intent understanding, department routing and the booking flow — handing off to a human when needed.
Where it started
The hospital handled much of its appointment and service routing with human operators. Most incoming requests were similar and repetitive, yet each required finding the right department and a suitable time.
Classic rule-based bots fell short on these requests: they couldn't grasp the patient's real intent, couldn't carry conversation context, and couldn't perform an actual booking.
How we approached it
We split the problem across multiple collaborating agents rather than a single model: a layer that understands intent, a triage agent that routes to the right department, an agent that runs the booking flow, and a knowledge agent that answers frequent questions.
These agents were connected on a workflow graph; in ambiguous or edge cases, the flow was designed to hand control to a human.
Technical solution
The prototype was built with Python; agent orchestration used LangGraph and LangChain, and the language capability used the OpenAI API.
Traceability of every step — which agent made which decision, which tool it called, and how long it took — was placed at the center of the architecture, keeping the system auditable and improvable.
Let the next project be yours
If you have a similar need, let's talk through how we could move forward in a short call.