Health decisions with full context

Taiiku

Taiiku required product craft across B2C, B2B2C and B2B modes. We shaped a domain model where training is not separated from nutrition, sleep, recovery, coach context and organizational operations.

Health and performance platform Training Nutrition Recovery Coaching Domains Professional Organizations Data Web app Mobile Cloud AI
Training session
Project Taiiku Company Japonics

Introduction

Inside Taiiku

Fitness tools are strong in single categories but weak at explaining readiness, risk and progress across the whole person.

What had to connect

The UX must serve a gym user during a set, a coach reviewing clients and an organization reading aggregate health operations.

B2C individual product B2B2C coach workflows B2B organization layer Signals readiness context

Features

Product features with technical depth

We designed Taiiku as a decision platform: data leads to coaching, adjustment and safer progression.

Training intelligence

Plans, execution and history connect to fatigue and progression.

Nutrition context

Food, macros and supplements respond to training and health goals.

Recovery signal

Sleep, HRV and readiness guide intensity decisions.

Coach workspace

Professionals see full context only with consent and clear boundaries.

AI coaching direction

Readiness, load and nutrition context can support recommendations with clear consent boundaries.

Workout execution

The active session is fast, offline-aware and low-friction.

Coach dashboard

Risk, adherence and recommendations are visible before messaging a client.

Readiness panel

Recovery and load tell users when to push and when to adapt.

Challenges

Fragmented health data

Training, food, sleep, biomarkers and coaching usually live in separate systems.

Technical challenges

01

Consent and health data

Coaches and organizations needed context only within clear consent boundaries.

02

Multi-audience UX

The same platform had to serve a gym user, coach and organization without flattening their workflows.

03

Signal context

Training, nutrition and recovery data had to explain readiness instead of producing isolated metrics.

Domain challenges

01

Fragmented health data

Training, food, sleep, biomarkers and coaching usually live in separate systems.

02

Workout execution

The active session is fast, offline-aware and low-friction.

03

Coach dashboard

Risk, adherence and recommendations are visible before messaging a client.

Technology stack

Technology stack behind the product

Taiiku groups training, nutrition, recovery, health, coaching, organizations, education and communication into bounded domains.

Domains Training, nutrition, recovery, health
Professional Coach planning, clients, guidance
Organizations Members, schedules, operations
Data Biometrics, analytics, recommendations
Web app Angular, coach and organization UX
Mobile .NET MAUI, workout and nutrition flows
Cloud GCP, Kubernetes, health workloads
AI Readiness and coaching recommendation direction
Training recovery Coach planning guidance schedules Biometrics recommendations coach and organization UX workout and nutrition flows Kubernetes Readiness and coaching recommendation direction Professional Data Mobile AI Training recovery Coach planning guidance schedules Biometrics recommendations coach and organization UX workout and nutrition flows Kubernetes Readiness and coaching recommendation direction Professional Data Mobile AI
nutrition health clients Members operations analytics Angular .NET MAUI GCP health workloads Domains Organizations Web app Cloud Coaching nutrition health clients Members operations analytics Angular .NET MAUI GCP health workloads Domains Organizations Web app Cloud Coaching

Japonics

Build health software that sees the whole system, not one metric

Taiiku is a case study in multi-audience platform design and performance data UX.