# UIOS v1.1 – Real World Validation

**Version:** 1.1.0  
**Date:** 2026-06-28  
**Status:** Specification  
**Previous:** UIOS v1.0 (Architecture)

---

## Overview

UIOS v1.1 shifts focus from architecture to **proving the system works with real data**. No new core components. Only validation, measurement, and hardening.

> "Architecture without validation is just a beautiful diagram."

---

## Five Goals

### 1. Production Capture iPhone App

**Current state:** Swift prototype exists (`ios/QuixZoomCapture/`)

**v1.1 requirements:**

| Feature | v1.0 | v1.1 |
|---------|------|------|
| Manual capture | ✅ | ✅ |
| Auto-upload | ❌ | ✅ |
| Background sync | ❌ | ✅ |
| Real-time GPS | ❌ | ✅ (±3m) |
| Sensor data | ❌ | ✅ (gyro, accelerometer, compass) |
| AI guidance | ❌ | ✅ ("Move closer", "Rotate 30°") |
| Offline mode | ❌ | ✅ (queue locally, sync when online) |
| Battery optimization | ❌ | ✅ |

**Key metric:** 95% of captures upload within 10 seconds on 4G.

**Implementation:**
- Background URLSession for uploads
- CoreLocation with kCLLocationAccuracyBest
- CoreMotion for sensor fusion
- Reachability monitoring for sync
- Local SQLite queue for offline

---

### 2. Bangkok Field Test

**Current state:** 6 videos processed, 45 observations, synthetic data dominates

**v1.1 requirements:**

| Metric | Target | Current |
|--------|--------|---------|
| Verified observations | 1,000+ | 45 |
| Object types covered | 6+ | 3 |
| GPS accuracy < 5m | 90% | Unknown |
| Image quality > 0.8 | 95% | Unknown |
| Multi-zoomer coverage | 3+ people | 1 |
| Time-of-day coverage | Day/evening/night | Day only |

**Test protocol:**
1. Recruit 3 Zoomers in Bangkok
2. Assign missions from Mission Planner
3. Capture with production app
4. Upload to S3 → Pipeline → OIE → UKG
5. Human review of all observations
6. Measure: precision, recall, merge rate, latency

**Success criteria:**
- OIE precision > 95% on real data
- OIE recall > 90% on real data
- Average 3+ observations per object
- Coverage score > 60% for all categories

---

### 3. End-to-End Pipeline Validation

**Current state:** Components work individually, limited integration testing

**v1.1 requirements:**

Test the complete flow:

```
Capture → Upload → AI Detection → OIE → UKG → Change Detection →
Coverage Update → Mission Generation → Zoomer Notification
```

**Validation scenarios:**

| Scenario | Expected Result |
|----------|----------------|
| New street lamp observed | Object created in UKG |
| Same lamp observed twice | Observations merged |
| Lamp with rust vs. repaired | Change detected, health updated |
| Coverage gap identified | Mission generated |
| Mission completed | Coverage score updated |

**Performance targets:**

| Stage | Target Latency |
|-------|---------------|
| Upload to S3 | < 5s |
| AI detection | < 2s |
| OIE processing | < 100ms |
| UKG update | < 50ms |
| Mission generation | < 1s |
| **Total: Capture → Mission** | **< 10s** |

---

### 4. OIE Performance Measurement

**Current state:** Benchmarks on synthetic data show 100% precision/recall

**v1.1 requirements:** Measure on real data with known ground truth.

**Method:**
1. Select 50 objects in Bangkok (known locations)
2. Have 3 Zoomers capture each object 3 times (9 observations/object)
3. Run OIE on all 450 observations
4. Compare OIE output to known ground truth

**Metrics:**

| Metric | Target | Measurement |
|--------|--------|-------------|
| Precision | > 95% | True merges / All merges |
| Recall | > 90% | Found objects / Known objects |
| False merge rate | < 2% | Wrong merges / All merges |
| Missed merge rate | < 5% | Missed merges / Should merge |
| Latency (P99) | < 50ms | Time per observation |

**Tuning:**
- Adjust weights based on real-world performance
- Test different configurations
- Document optimal settings per city type

---

### 5. Customer Demo Data

**Current state:** Synthetic data, no customer-facing outputs

**v1.1 requirements:** Real data that demonstrates value.

**Deliverables:**

| Deliverable | Content |
|-------------|---------|
| Bangkok City Report | Coverage map, object inventory, health scores |
| Change Detection Report | Before/after comparisons, alerts |
| Mission Effectiveness | Missions completed, coverage improved |
| API Demo | Live endpoints with real data |

**Customer pitch:**
> "We deployed 3 Zoomers in Bangkok for 2 weeks. They captured 1,000+ observations covering 300+ objects. Our AI identified 15 changes, including 3 safety issues. Here's the data."

---

## Timeline

| Week | Focus | Deliverable |
|------|-------|-------------|
| 1 | iOS app hardening | Production-ready app |
| 2 | Bangkok recruitment | 3 Zoomers onboarded |
| 3-4 | Field test execution | 1,000+ observations |
| 5 | Pipeline validation | End-to-end test passed |
| 6 | OIE measurement | Benchmark report |
| 7 | Customer demo prep | Presentation + data |
| 8 | Review & plan v1.2 | UIOS v1.1 retrospective |

---

## Success Criteria

UIOS v1.1 is successful when:

1. ✅ iOS app captures and uploads with < 10s latency
2. ✅ 1,000+ verified observations from Bangkok
3. ✅ End-to-end pipeline processes without manual intervention
4. ✅ OIE precision > 95%, recall > 90% on real data
5. ✅ Customer demo shows clear value proposition

---

## Risks & Mitigation

| Risk | Impact | Mitigation |
|------|--------|------------|
| Zoomer recruitment fails | High | Start early, offer competitive pay |
| iOS app crashes in field | High | Extensive testing, offline fallback |
| OIE performs poorly on real data | High | Manual review, iterative tuning |
| Network issues in Bangkok | Medium | Offline mode, batch sync |
| Data quality poor | Medium | Quality control, re-capture missions |

---

## v1.2 Preview

If v1.1 succeeds:

- **v1.2:** Multi-city deployment (Torrevieja, Stockholm)
- **v1.3:** Customer API launch
- **v1.5:** Model marketplace
- **v2.0:** Autonomous mission planning

---

**End of v1.1 Specification**