Grounded information
Responses needed relevant context from a structured health knowledge base.
Anonymous healthcare AI case study
Building a context-aware AI health assistant
A mobile AI agent connected grounded health information, nearby care discovery, service actions, and memory so each interaction did not start from zero.
The situation
Health-related questions often require both reliable information and a practical next action.
Mobile users may need relevant guidance when local knowledge is limited and time matters.
A useful assistant therefore had to retrieve from a structured knowledge base, connect to care-related services, and preserve appropriate context between interactions.
The challenge
Ground responses, connect useful services, and preserve context without presenting the assistant as a clinician.
The product had to coordinate knowledge retrieval and actions within a clear information-support boundary.
Connected actions
Users needed pathways to nearby care, medication support, and emergency assistance.
Continuity
Short- and long-term memory had to reduce repetitive interactions while keeping context manageable.
The solution
An Agentic RAG workflow coordinated retrieval, service tools, and memory around the user’s immediate need.
The assistant combined conversational context with selected information sources and care-navigation functions.
- 01
Retrieve relevant context
Use Agentic RAG to identify information related to the current request.
- 02
Connect service tools
Coordinate nearby care discovery, medication-support, and emergency-assistance workflows.
- 03
Preserve useful memory
Combine short- and long-term context so follow-up interactions can remain coherent.
How the work was structured
The workflow separated information retrieval, service actions, and conversational memory.
Retrieval supplied grounded context instead of relying only on the model’s general response behavior.
Tool-connected functions gave the assistant structured ways to help users navigate relevant services.
Memory supported continuity while the product remained positioned as guidance and navigation rather than medical diagnosis.
01Understand
Interpret the current request and context.
02Retrieve
Find relevant knowledge.
03Connect
Offer an appropriate service pathway.
04Remember
Preserve useful interaction context.
The result
The product connected grounded guidance, service navigation, and conversational continuity in one mobile experience.
The documented implementation gave the assistant a structured path from user context to retrieved information and available care-related actions.
Grounded retrievalthrough an Agentic RAG workflow
Connected servicesfor nearby care and support pathways
Conversation continuitythrough short- and long-term memory
Information became actionable
The assistant connected retrieved context with relevant navigation and support functions.
Interactions could continue
Memory reduced the need to repeat appropriate context in each conversation.
The boundary stayed clear
The product supported information and navigation rather than clinical diagnosis.
Case taxonomy
Searchable by industry, technology, product, and business need.
Industry and product
- Healthcare
- Mobile Health
- AI Assistant
- Care Navigation
Technology and delivery
- AI Agent
- Agentic RAG
- Tool Integration
- Long-term Memory
- Short-term Memory
Business need
- Grounded Guidance
- Nearby Care Discovery
- Medication Support
- Emergency Assistance
- Context Continuity
Connect enterprise knowledge to useful actions
Need an AI assistant that works inside a governed healthcare workflow?
Get in touch
Ready to build software that fits your business?
Tell us what you need to build, modernize, automate, or augment with AI. We can start with a focused discussion or a no-risk 1-week trial.
“A fantastic company to work with.” After the initial rapid development project, American Shipping Co. kept two Shinetech developers embedded for nearly four years, supporting internal and external tools and new AI initiatives.
CEO, American Shipping Co. - 5-star Google Review