01
Structured sleep data
The cards are created from defined data points instead of from free chat context.
Case studies sleep²
An AI Sleep Coach for personalized sleep analysis.
sleep² is a sleep app. One of its founders is a sleep researcher; that expertise shapes the product logic, glossary, and recommendations.
thirdmind developed an AI system that translates structured sleep data into understandable sleep analysis cards. Today, tens of thousands of sleep cards are created every day in more than 15 languages.
Product view Making sleep understandable Sleep² shows sleep quality, influencing factors and concrete tips for improving nighttime recovery.
Two sleep² app screens with sleep duration, sleep quality and personalized alerts 01 Why AI
Why generative AI makes sense here.
Sleep data is personal. Two people can have similar numbers and still need different guidance. Generative AI can help here when it does not write freely, but works with data, product logic, and clear language.
02
Multilingual output
The system generates sleep cards in more than 15 languages while keeping the same technical logic in the background.
03
Glossary fidelity
A key task was to ensure that the AI used sleep terms exactly as they were defined in the product.
02 system
Tens of thousands of sleep cards every day.
The AI Sleep Coach runs as a productive system inside the sleep² app. Personalized cards are created from existing sleep data and explain what stands out in the user's own language.
- 01 · Data
Sleep data from the app is read.
Structured fields, defined sources.
- 02 · Pattern
Anomalies and developments are recognized.
What is new, what repeats, and where patterns shift.
- 03 · Evaluation
Analysis perspectives emerge from patterns.
Structured building blocks for the card.
- 04 · Glossary
Terms and tone follow fixed guidelines.
Sleep vocabulary, terminology, and wording are not arbitrary.
- 05 · Edition
The card is generated in the appropriate language.
Personalized evaluation directly in the product.
Free texting was not the goal here. The aim was to create an explanation that comes from existing data and remains technically consistent at high volume.
03 Tasks
What the AI Sleep Coach does.
In productive operations, a single good card is not enough. The system must generate many cards, remain consistent across languages and adhere to the technical framework.
- 01
Read structured sleep data
- 02
Recognize patterns and anomalies
- 03
Define analysis perspectives
- 04
Generate tens of thousands of Sleep Analysis Cards every day
- 05
Generate output in more than 15 languages
- 06
Follow glossary, tone, and medical boundaries
The difficult work often lies in consistency. A single good card is not enough. The system must remain within the technical framework even when volume is high.
04 control
The system stays within the domain vocabulary.
One of the main tasks with Sleep2 was to keep the output close to the agreed glossary. A sleep coach can sound personal, but it must not improvise technical terms, recommendations, or product logic.
01
Glossary binding
Terms are used as they are technically defined in the product.
02
Sleep expertise
Sleep2 brings expertise from sleep research into product logic, glossary and tonality.
03
Medical boundary
The cards explain sleep data and do not replace diagnosis or medical advice.
04
Product integration
Works within the app, not as a loose chatbot.
05
Multilingualism
More than 15 languages without diluting the technical meaning.
06
Scaling
Tens of thousands of cards every day, without manual individual formulation.
05 Approach
From prototype to productive card.
We start with the existing sleep data and a clear question: which card should be created, for which situation, in which language, and with which tone.
Then we build the output logic so the data, glossary, and recommendation framework fit together. The glossary matters: the AI must use terms exactly as Sleep2 specifies them.
If the cards are created reliably, the system is integrated productively into the app. Expansion comes only when volume, language, and domain quality are stable.
06 FAQ
Questions about the sleep² case study.
What did thirdmind build for sleep²?
thirdmind built an AI Sleep Coach that evaluates sleep data and generates personalized Sleep Analysis Cards directly in the app. The system creates tens of thousands of cards every day in more than 15 languages.
Is the AI Sleep Coach a medical system?
No. It is a system for understandable evaluation within a sleep app. It does not replace medical diagnosis or advice.
Why is generative AI suitable for Sleep Analysis Cards?
Because sleep data needs to be explained. Generative AI translates structured data into understandable, personal language as long as output logic, glossary and technical boundaries are clearly defined.
Is the system productive?
Yes. The AI Sleep Coach runs productively in the sleep² app and generates tens of thousands of sleep analysis cards every day.
What role does the glossary play?
A central role. One of the sleep² founders is a sleep researcher, so the AI has to use terms and recommendations exactly as intended in the product.
Can the principle be transferred?
Yes, wherever users regularly generate data and need understandable evaluation: health, coaching, learning, fitness, or B2B dashboards that require explanation.
Make product data understandable
You have product data, but the evaluation could be clearer?
Talk about a similar processYuno asks four short questions about product, data, target group, and risk. After that, it is clearer whether an AI system makes sense for evaluation or coaching.