AI creates capacity in administration and communication
Patient communication, education content, and appointment scheduling tie up staff who are needed for care. We build agents for these functions, not for clinical decisions.
AI levers in healthcare
Healthcare companies and practices work with limited staff capacity and heavy communication load: appointment requests, recurring questions about treatment or preparation, education material that has to be made understandable for every audience. This effort competes directly with time for treatment and care.
We build AI into administrative and communication processes without touching clinical decisions: appointment scheduling, answering recurring patient questions, preparing education and information material. With FoodCheck and FitWise we've built our own AI-assisted health and nutrition products with LLM integration, and we know how to turn technical source material into understandable, reliable content. Clinical decision systems subject to regulatory approval are not our field; we say that openly to prospects before a project starts.
Functions with the largest lever
- 1
Customer service
An agent answers recurring patient questions on appointments, preparation, and procedures
- 2
Marketing & content
Education and information material comes from technical source material, editorially reviewed
- 3
Appointment scheduling
Agents coordinate appointment requests and reminders through existing communication channels
- 4
Finance & reporting
Automated preparation of billing and utilization data by location or treatment type
- 5
Software development
LLM integration into our own and client-specific healthcare applications, from our own product work
→ Capacity
Typical use cases
Patient service agent
Answers recurring questions on appointments, treatment preparation, and practice procedures directly from existing documentation.
Education material from technical content
Generates understandable patient information from technical source material, reviewed before release.
Appointment coordination
Automated appointment reminders and rescheduling requests through existing communication channels.
Billing and utilization reporting
Automated summary of billing and utilization data for location and practice management.
LLM integration into healthcare applications
Design and implementation of AI features in existing or new health and nutrition applications.
Prerequisites
| Area | Requirement | Typical state |
|---|---|---|
| Data | Patient communication and education material structured, with no special categories of personal data in the agent's access | Varies; the separation between administrative and medical data is often not drawn cleanly |
| Systems | Practice or clinic management system with controlled API access | Often in place, with a limited or poorly documented interface |
| Organization | One accountable person who sets the scope of the agent's role and the privacy boundaries | Should already exist via data protection officers; the role for AI agents usually needs to be newly defined |
Requirement: Patient communication and education material structured, with no special categories of personal data in the agent's access
Typical state: Varies; the separation between administrative and medical data is often not drawn cleanly
Requirement: Practice or clinic management system with controlled API access
Typical state: Often in place, with a limited or poorly documented interface
Requirement: One accountable person who sets the scope of the agent's role and the privacy boundaries
Typical state: Should already exist via data protection officers; the role for AI agents usually needs to be newly defined
Our approach in healthcare
We start with a clear boundary: which processes are administrative or communicative and therefore suited to an agent, and which touch medical decisions and stay with clinical staff. We implement only the first category, with particular care for personal health data. We put this boundary in writing before a project starts.
| Ausgangslage | Umsetzung | Ergebnis | |
|---|---|---|---|
| Own products: FoodCheck and FitWise | Own product idea in nutrition and fitness, with AI as the core function. | Flutter apps with OpenAI integration, own backend APIs, own AI services. | Two own AI products in production, fully built and operated in-house. |
| EdTech company, customer service | Customer service answered recurring questions manually from scattered knowledge. | AI agent with access to the knowledge base, with clear escalation to humans. | Recurring inquiries run productively through the agent, complex cases stay with the team. |
| EdTech company, content production | Content production ran step by step by hand and depended on a few people. | n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off. | The content team works on quality, not on mechanical intermediate steps. |
Own products: FoodCheck and FitWise
Ausgangslage
Own product idea in nutrition and fitness, with AI as the core function.
Umsetzung
Flutter apps with OpenAI integration, own backend APIs, own AI services.
Ergebnis
Two own AI products in production, fully built and operated in-house.
EdTech company, customer service
Ausgangslage
Customer service answered recurring questions manually from scattered knowledge.
Umsetzung
AI agent with access to the knowledge base, with clear escalation to humans.
Ergebnis
Recurring inquiries run productively through the agent, complex cases stay with the team.
EdTech company, content production
Ausgangslage
Content production ran step by step by hand and depended on a few people.
Umsetzung
n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off.
Ergebnis
The content team works on quality, not on mechanical intermediate steps.
Frequently asked questions
Initial call: 30 minutes, concrete.
We look at your patient communication and education processes and tell you honestly where an agent fits.