Get in touch
Managing healthcare operations at scale takes precision and speed. For a U.S.-based healthcare organization with 70,000+ employees, that balance was hard to maintain. Call centers were flooded with repetitive, region-specific questions about insurance, medical leave, and policies. It resulted in slower response times and growing pressure on support teams.
To change that, the company partnered with Master of Code Global. Together, we created an intelligent FAQ chatbot that understands healthcare terminology, adapts to local regulations, and delivers instant answers. Now, employees across four states can access accurate information in seconds, freeing teams to focus on what matters most: delivering exceptional care.
For a healthcare company serving tens of thousands of employees across Wisconsin, Minnesota, Arizona, and Florida, internal knowledge access was anything but simple. Every region operated under different policies, legal requirements, and HR rules, making it difficult for staff to get accurate answers quickly. Call centers were flooded with repetitive questions about maternity leave, insurance options, or amount of sick days available.
Even with a vast internal knowledge base in place, the volume and fragmentation of information created confusion and bottlenecks. Existing AI tools weren’t cutting it either. The company had been using a legacy language model that struggled with nuance, lacked regional context, and couldn’t interpret healthcare-specific terminology reliably.
Their objective was to reduce the burden on call center agents by implementing a smarter, more context-aware solution that could handle complex FAQs – accurately, instantly, and at scale.
Master of Code Global built a region-aware FAQ chatbot that speaks healthcare, understands context, and scales to support 70,000 employees
To tackle fragmented knowledge access and reduce the load on internal support teams, we developed a powerful AI-based FAQ chatbot designed specifically for the medical sector. Built with OpenAI o4-mini and fine-tuned for performance, the assistant delivers accurate, conversational responses to employee inquiries, while accounting for regional regulations, specific terminology, and organizational hierarchies.
We started by restructuring the client’s vast knowledge base, segmenting it by both state-specific policies and user roles (supervisors vs. non-supervisors). This made sure that the bot could surface the right answer depending on who was asking and where they were located.
To replace the client’s outdated LLM, we implemented a more capable language model that handles small talk gracefully, understands medical phrasing, and retrieves precise answers with regional nuance. This instantly elevated the quality of interactions, making the chatbot not only helpful, but trusted.
The application is incredibly user-friendly and offers a seamless experience for both customers and agents. It facilitates efficient interactions with customers, making it an excellent solution for addressing inquiries in a modern way.
Instantly identifies the user’s location (e.g., Florida vs. Minnesota) and tailors responses based on applicable policies and legal frameworks.
Differentiates between supervisors and regular employees to provide contextually relevant answers.
Handles medical and HR-related terminology with accuracy – from insurance jargon to benefits eligibility questions.
Answers high-frequency questions around maternity leave, sick days, insurance plans, and more, freeing up call center agents for complex requests.
Designed with integration in mind, the next step includes connecting the chatbot to the company’s CRM to enable personalized, case-specific support (e.g., “How many vacation days do I have left?”).
