Swae OS
Health Platform. Federated Backend. AI-Ready.
Workout programming, habit tracking, journaling — with architecture that scales to AI coaching
Screenshots
Results
Programs
Coach → Client
templates, assignments, marketplace
Habits
Beyond workouts
lessons, gratitude, reflection
Federated
Backend
GQL stitching via Hive Gateway
Most fitness apps are either too simple (just log sets) or too complex (overwhelming dashboards nobody uses). Swae sits in the middle: structured enough for serious programming, simple enough to actually use.
Coaches create workout templates and programs, assign them to clients, publish to a marketplace. Clients enroll, log workouts, track habits. Exercise library with videos for every movement. Morning gratitude, evening reflection, freeform journaling. Meal tracking.
The interesting part is the backend. GraphQL federation via Hive Gateway stitches multiple services together. The architecture supports a multi-backend RAG system — Postgres for structured data, Qdrant for vectors, Memgraph for relationships.
The AI coaching layer isn't live yet. Due to how fast agentic AI has evolved since I started building, the original agent service got scrapped. The architecture is ready; when there's a product reason to spin up AI coaching, it's a deployment away. For now, the platform does what a health platform should do — track workouts, build habits, reflect.
For Engineers
Architecture
Three federated services behind a Hive gateway. User service handles auth and profiles. Tracking service handles high-write workout/meal logging. Knowledge service architecture supports hybrid RAG (BM25 + vector) — currently dormant, ready for AI coaching when product demands it. Event-driven async indexing via direct HTTP calls from journal service (no Celery/pub-sub overhead on GCP).
View diagram
┌─────────────────────────────────────────┐
│ Hive Gateway │
│ (GraphQL Federation) │
└─────────────┬───────────────────────────┘
│
┌───────────┼───────────┐
↓ ↓ ↓
┌─────┐ ┌─────┐ ┌─────────┐
│User │ │Track│ │Knowledge│
│Svc │ │Svc │ │Svc │
└──┬──┘ └──┬──┘ └────┬────┘
│ │ │
↓ ↓ ↓
Postgres Postgres Qdrant
(ready)
Key Decisions
Federation Over Monolith
Health data has different access patterns. Workouts are high-write, low-read. Knowledge queries are read-heavy, latency-sensitive. Federation lets each service optimize independently.
AI-Ready, Not AI-First
Built the data layer and architecture first. The coaching layer can slot in when there's a real product reason — not just because AI is trendy.
Habits Beyond Workouts
Fitness is behavior change, not just sets and reps. Gratitude journaling, evening reflection, lesson programs — the platform supports holistic coaching, not just workout logging.
What Was Hard
Keeping the architecture clean while the product scope shifts. The original AI coaching vision is on hold; the challenge is maintaining a useful standalone product while the AI layer waits.
Stack