About our technology
The daily life of adults 55+ only becomes meaningful when it accumulates in their own voice.
General-purpose LLMs struggle to read how this generation expresses their day. The slow speech, the dialect, the context carried over from yesterday. We accumulate, every day, the way they actually speak and the everyday context they live in.
We start with Korean speakers aged 55+. The same structure extends to the languages and generations of global markets.
- Everyday context · “My knee hurt yesterday” changes today’s suggestion.
- A generation’s way of speaking · not a translation, but understanding the words it actually uses.
- Data that accumulates · the more it is used, the more of this generation’s expression patterns build into an asset.
One ordinary conversation is all it takes
Start a relaxed conversation in the app, and you get suggestions tailored to your day, delivered as cards.
The final call always belongs to the user.
The deeper an Agent gets involved, as it does when applying on someone’s behalf, the more directly the outcome touches the user’s actual day. That is why, even in areas we could technically automate, we designed the go/no-go call right before execution to always stay with the user.
“I’d like to learn something new next week”
intentIntentlearn · something new · next week, AM
memoryRecallMunjeong-dong · prefers mornings · last week: stretching
searchSearch activity DB2 standout picks from 7,057 activities in our DB
planNext week: day designed
- Tue 10:00 · Botanical Art · lifelong-learning class
- Thu 09:30 · AI & Digital Basics · run by DailyFit
From a single spoken sentence to a day’s plan
A user’s words pass through four layers and turn into a designed day. Every one of them runs in the live service, every day.
- Layer 1User ChannelReal-time speech-to-text (STT) · text · Kakao login
- Layer 2AgentsDiscovery · reminder · application-relay Agent orchestration
- Layer 3DataProfile · per-user memory · search and matching
- Layer 4ExternalActivity database (public OpenAPI · scrapers · self-supplied)
The data belongs to the user
Least privilege
Only the information we need, only at the moment we need it.
Explicit consent
We tell you first what we use, and act only within the scope you allow.
Privacy-law compliance
Stored and encrypted safely, to the standard of the Personal Information Protection Act (PIPA).
Personal data sits under several layers of defense
We handle the details of our security architecture with care. What we share openly are the principles we hold ourselves to.
Least-privilege principle
Every system account holds only the minimum permissions it needs. Data access is separated by role.
End-to-end encryption
Personal data is encrypted both in transit and at rest.
Sensitive-data isolation
Sensitive data is handled only inside a separated boundary, and the surface exposed to the outside is minimized.
Always-on audit and response
Sensitive operations are logged and reviewed. We assume attack and place defenses first.
All of these safeguards operate on top of the Personal Information Protection Act (PIPA) standard.
DailyFit’s Moat
The more it is used, the more deeply it understands each user. That depth of understanding is our moat.
Raw Conversation Insight
The raw, unfiltered ‘everyday conversation’ of adults 55+. Our biggest moat.
Increasing Personalization
Tastes, history, and movement patterns build up per user. The more it is used, the better the fit, and a general-purpose model cannot replicate this layer.
Data Flywheel
The more data accumulates, the smarter the Agent gets, and the more it is used, the more data accumulates. The gap widens over time.
The company itself is an AI Agent team
In the product, AI Agents help design a member’s day, and in operations, an AI Agent team helps run the company. Strategy · Finance · Product · Technology. An AI Agent team documents each Division in ADRs and operates it together.
CEO Office
Strategy
Finance
Product
Technology
Marketing
Intern Team
1 human · 1 orchestrator · 15+ agents · 6 divisions · More to come!