Independent Product Engineering Studio
I design and build scalable software systems for startups and small teams — from architecture to production deployment. Specialising in AI-native iOS apps that run all inference on-device using Apple's Neural Engine — delivering privacy, speed, and zero API costs.
Trusted Technologies
Real systems delivered in production. From complex business platforms to developer tooling.
I work with specific types of teams and projects. Clear fit means better outcomes.
Productized service offerings with clear deliverables, fixed timelines, and senior-level execution.
Deep technical review of your codebase. Get a prioritized roadmap, AI readiness evaluation, and actionable next steps.
→ Prioritized technical roadmap
Production-grade foundation built in a fixed sprint. SwiftUI architecture, scalable data layer, and App Store-ready delivery.
→ App Store-ready iOS/iPadOS app
Private, on-device AI built into real products. Integrate Apple Foundation Models or local LLMs with performance optimization and privacy-first architecture.
→ Production AI feature, privacy-first
Real systems built through 3NSOFTS. Problem → System → Outcome.
Problem
SMEs juggling spreadsheets, messaging apps, and paper notes for inventory and dispatch.
System
Native iOS/iPadOS app with offline-first Core Data + CloudKit sync for seamless team collaboration.
Outcome
Single source of truth eliminated data fragmentation and communication overhead.
Developer Tool
Problem
iOS developers waste hours debugging Xcode config issues that lead to build failures and App Store rejections.
System
Native macOS app performing 9 specialized checks in under 2 seconds with read-only analysis.
Outcome
Developers catch configuration errors before builds fail, eliminating hours of debugging.
Offline AI Assistant
Problem
Emergency scenarios require AI assistance without internet connectivity or privacy compromises.
System
100% on-device AI with Apple Intelligence, battery-aware architecture, and zero cloud dependency.
Outcome
Reliable AI guidance in offline scenarios with privacy-first design and extended battery life.
Technical Differentiation
What makes 3Nsofts different from typical iOS development shops.
Offline-first data strategies built on Core Data + CloudKit. Apps write to a local store first and sync in the background — users never wait for a network response. Conflict resolution, merge policies, and partial sync are designed from the start, not retrofitted when scale problems appear.
AI inference that runs entirely on the user's device using Core ML and Apple Foundation Models. No data leaves the device. No API costs. No latency from a round-trip to a server. Battery-aware scheduling and model quantization keep AI features fast without draining the battery.
Production-grade SwiftUI architectures with clean data flow, modular view hierarchies, and components that can be tested in isolation. The distinction matters in practice: prototype-quality SwiftUI code accumulates view model debt quickly. Systems-quality SwiftUI scales to dozens of screens without rewrites.
Architecture decisions are documented as they are made — not reconstructed later. Dependencies are minimal and intentional. Naming reflects domain concepts, not implementation details. When requirements change (and they do), the system accommodates them without structural surgery. Code that the next engineer can understand is not a bonus — it is part of the deliverable.
Product Ecosystem
12+ production apps and tools designed and shipped through 3NSOFTS — each one demonstrating a specific architectural decision in practice.
Sorto — on-device email classification using Core ML inference, zero server dependency. offgrid:AI — fully offline AI assistant with local LLM and battery-aware scheduling. Xcode Doctor — static analysis tool that diagnoses Xcode configuration errors in under 2 seconds. CalmLedger — financial tracking with on-device data, no account required. SnipToCode — design-to-code SaaS with real-time AI streaming and Paddle billing.
These are not demos. Each is in production, distributed through the App Store or direct download, and maintained with the same standards applied to client work.
See all products→Production-grade iOS, iPadOS, and macOS apps — primarily systems that handle complex data, offline-first workflows, and on-device AI. Not simple CRUD apps or quick prototypes.
Yes, if the product problem is defined and the team is serious about shipping. The MVP Sprint engagement is structured specifically for early-stage products that need a solid technical foundation from day one.
Software where the AI capability is built into the core data and interaction model — not added as a feature after the fact. On-device inference via Core ML or Apple Foundation Models, tightly integrated with the app's data layer.
The Architecture Audit delivers in 5 business days. The MVP Sprint runs 6–8 weeks. On-Device AI Integration runs 3–4 weeks. All engagements are fixed-scope and fixed-price.
On-device AI runs inference directly on the user's hardware using Apple's Neural Engine — no cloud round-trip, no data leaving the device. This means AI features work offline, respond faster, and require no API keys or ongoing cloud spend.
New engagements start with a structured application to ensure strong product and technical alignment.
Other inquiries
For partnerships, press, or non-project inquiries: info@3nsofts.com
