Paw Trail

When I started Paw Trail, I wanted to solve a simple but frustrating problem: dog-walking apps were either too generic, too fitness-only, or too limited for real day-to-day dog owners. I wanted one app that could help people plan better walks, track them reliably, and learn from their routines over time.

Paw Trail is now live on the App Store. This post is a behind-the-scenes look at the product decisions, technical architecture, and lessons learned while building it.

Project Snapshot

Paw Trail is an iOS app focused on the full dog-walk lifecycle: before the walk (planning), during the walk (live tracking), and after the walk (insights and review). The app is built in Swift with a modular architecture so features can evolve without creating one giant codebase bottleneck.

The Problem I Wanted to Solve

Most dog owners do not need another complicated health dashboard. They need confidence: where to walk, how far they walked, how each dog is doing, and whether something in the area could be unsafe.

The product goal became:make daily dog walks easier to plan, safer to execute, and more meaningful to review.

"Good consumer apps remove uncertainty. Great consumer apps remove uncertainty at exactly the right moment."

In Paw Trail, that translated into three design priorities:

1. Fast start: users should start tracking in seconds.
2. Reliable data: location quality should be strong enough for real-world movement and noisy outdoor conditions.
3. Actionable feedback: post-walk views should teach users something, not just store numbers.

Product Experience: Before, During, and After a Walk

Before the walk: planning and confidence

The planning flow focuses on what users care about before leaving home:

• nearby route suggestions (upcoming)
• weather context and forecast signals
• upcoming walk scheduling
• quick access to the dogs involved in the walk

This gives users a clear "should I go now?" answer today, with "where should I go?" expanding as nearby route suggestions roll out.

During the walk: low-friction live tracking

During active tracking, Paw Trail emphasizes clarity over clutter:

• live map and route progression
• distance and pace updates
• elevation, ascent, and descent information
• persistent walk state surfaced in Live Activity

The design objective here was consistency: users should never wonder whether tracking is still active.

After the walk: insights and memory

After a walk, users can review details and identify patterns:

• route summary and key stats
• dog-level performance trends
• historical comparisons (daily/weekly)
• optional route publishing and sharing behavior (upcoming)

This transforms one walk into a useful data point in a bigger routine.

Technical Architecture

I built the app with a modular structure and clear feature boundaries. Feature modules (like Home, Walks, Distances, and Onboarding) are separated from manager modules (Location, Weather, Networking, Photos, and Walk logic), with shared models and infrastructure modules for cross-cutting concerns.

At the state-management layer, the app uses a modern reducer-oriented approach and async effects. This kept feature logic predictable while still handling real-time streams like location updates.

@Reducer
struct WalkFeature {
    @ObservableState
    struct State: Equatable {
        var isTracking = false
        var distanceMeters: Double = 0
    }

    enum Action {
        case startTapped
        case stopTapped
        case locationUpdated(Double)
    }

    var body: some ReducerOf<Self> {
        Reduce { state, action in
            switch action {
            case .startTapped:
                state.isTracking = true
                return .none
            case .stopTapped:
                state.isTracking = false
                return .none
            case let .locationUpdated(distance):
                state.distanceMeters = distance
                return .none
            }
        }
    }
}

Core stack choices

• SwiftUI for composable, fast UI iteration
• reducer-driven state architecture for predictable feature logic
• async/await for modern concurrency and cleaner async flows
• local persistence + cloud-backed services for responsive UX and sync
• RevenueCat for subscription/paywall management
• WidgetKit + ActivityKit for lock screen/home screen surfaces

Challenges and How I Solved Them

1) Reliable location under real-world conditions

Raw GPS streams are noisy, especially at walk start or in dense urban areas. I added accuracy-tier filtering, freshness checks, warm-up handling, and smoothing/rate-limiting behavior to avoid jumpy routes and unstable pace output.

Result: better route quality and fewer user-visible spikes in metrics.

2) Keeping the experience responsive while tracking continuously

Walk tracking touches many systems at once: location, persistence, weather snapshots, and UI updates. I separated responsibilities into focused manager modules and used actor-based boundaries for shared async state.

Result: cleaner feature code and fewer cross-module coupling issues.

3) Shipping monetization without blocking core value

I used RevenueCat to manage products and offerings while keeping the core walking experience coherent. That let me iterate on paywall and package strategy without rewriting purchase infrastructure.

Note: separating subscription configuration from app logic made experiments safer and faster.

4) Making "analytics" feel human

A common pitfall in tracking apps is data overload. I focused analytics views on understandable trends: walk counts, distance progression, and dog-specific patterns.

Result: the insights screens support decisions, not just vanity metrics.

What This Project Taught Me

• Product quality is mostly consistency in small moments, not only big features.
• Reliable background/location behavior is a product feature, not just technical plumbing.
• A modular architecture pays off quickly once the feature surface grows.
• Safety and trust signals can become key differentiators in lifestyle apps.
• Shipping to real users changes prioritization in a good way.

What I Plan to Improve Next

1. Expand route quality and recommendation logic with stronger personalization.
2. Improve safety flows with richer local hazard context and reporting UX.
3. Add deeper comparative insights across dogs, seasons, and walk types.
4. Continue refining onboarding to reduce setup friction for first-time users.

If you are building an iOS product where real-time context, reliability, and user trust matter, Paw Trail is the best example of how I approach product engineering end to end.