Case Study
Grounded Guidance
Expert-validated wellness coaching with hallucination controls.
Client
Wellness technology company
Market
Consumer wellness technology
Scale
Multi-wearable data across a consumer user base
Duration
6 months
Team
4 engineers
Engagement model
Managed Delivery
Delivery hub
Zagreb
Stack
GPT-class models, n8n agentic orchestration, Flowise, multi-wearable APIs
Capability
AI Assurance
Challenges
Challenges
Unsafe advice risk
A coaching model that hallucinates health guidance is a liability, not a feature.
Fragmented wearables
Signals arrived from multiple wearable APIs in different shapes.
Trust
Guidance had to be defensible to domain experts, not just plausible.
The solution
Grounding architecture
Responses grounded against validated sources, with conservative fallback behaviour.
Agentic orchestration
n8n and Flowise pipelines coordinating retrieval, checks and generation.
Expert validation loop
Domain experts review and sign off on guidance the system produces.
The honest section
What we would tell you before starting
Grounding and conservative fallback make the model decline more often. It says “I can’t advise on that” where an ungrounded model would improvise. For health guidance that is the correct trade, and we set that expectation with the client up front.