NCompas builds AI-powered iOS, Android, and cross-platform applications — from streaming in-app copilots and real-time computer vision to offline ML inference and AR experiences — delivered in production in 3–8 weeks.
Why NCompas
Retention is the only metric that matters in mobile. Everything we build is designed to give users a reason to open the app tomorrow.
We design for AI from the first architecture decision — on-device model selection, data pipeline for personalisation, streaming API integration, and offline AI capability baked in. AI features in mobile have unique constraints (battery, memory, latency) that require purpose-built architecture, not retrofitted widgets.
We don't have a single-framework religion. React Native, Flutter, Swift, and Kotlin are all production-capability for our engineers. We choose the right tool for your context — not the tool our team prefers, not the tool we've already paid for licences for.
Not every AI feature should call a cloud API. We know when to run on-device (latency-critical, privacy-sensitive, offline-needed) and when to call the cloud (complex reasoning, large models). And when we run on-device, we know how to quantise, prune, and optimise models to meet mobile memory and battery constraints.
Real mobile users go underground, lose signal, and use their phone on aircraft. Apps that break without connectivity aren't production-grade. We build offline-first — local-first data with sync, on-device AI inference, queued mutations, and graceful degradation — as the default, not a later requirement.
App Store Review Guidelines and Google Play policies are a maze of evolving rules — especially for apps using AI, health data, financial data, or user-generated content. We've shipped dozens of apps through both stores, including edge-case categories, and we know how to write review notes, handle rejections, and get approved first time.
Mobile apps handle some of the most sensitive data a user has — health, finance, location. OWASP Mobile Top 10, biometric auth, secure enclave storage, certificate pinning, jailbreak/root detection, and GDPR-compliant data handling aren't bolt-ons we add at the end. They're in the architecture from sprint zero.
Six capability domains
Six interconnected mobile engineering capabilities, each purpose-built for the constraints and opportunities of the device in your users' pockets.
One codebase, two production-quality apps — React Native and Flutter both deliver near-native performance with access to native device APIs, hardware sensors, and platform-specific capabilities. We choose the right framework for your context and build it to native quality standards, not web-wrapped compromise.
Intelligent mobile features that make your app genuinely useful — personalised content feeds, predictive search, conversational in-app assistants, real-time recommendations, anomaly alerts, and intelligent notifications that fire at the right moment based on behaviour, not a cron job.
One codebase, two production-quality apps — React Native and Flutter both deliver near-native performance with access to native device APIs, hardware sensors, and platform-specific capabilities. We choose the right framework for your context and build it to native quality standards, not web-wrapped compromise.
AI that runs entirely on the device — no round-trip to a cloud API, no latency, no privacy compromise. Core ML (iOS), TensorFlow Lite, PyTorch Mobile, and ONNX Runtime enable real-time inference for camera, audio, sensor, and text inputs. We train, quantise, and optimise models for mobile hardware.
Mobile cameras are the most powerful sensor your users carry. We build apps that use them — object detection, OCR, barcode/QR scanning, face recognition, medical imaging, AR product try-on, spatial measurement, and real-time video analysis. ARKit (iOS) and ARCore (Android) for augmented reality overlays that add real utility, not novelty.
When maximum performance, deep OS integration, or platform-specific capability demands native development, we build for it. SwiftUI and Swift Concurrency for iOS; Kotlin and Jetpack Compose for Android — with full access to HealthKit, SiriKit, App Clips, Widgets, Live Activities, and Android-specific capabilities.
Mobile apps live and die by start time, frame rate, and battery impact. We profile, optimise, and ship clean — then automate the delivery pipeline with Fastlane, GitHub Actions, and EAS Build. Security-first: certificate pinning, biometric authentication, secure enclave storage, and OWASP Mobile Top 10 compliance baked in.
Platform selection
No framework religion. We pick the right tool for your context — and we'll tell you if we think you're choosing the wrong one.
Best when: Your team knows JavaScript/TypeScript, you need both iOS and Android, and you want access to the npm ecosystem. Best for apps with complex state management and teams already using React.
Best when: You need pixel-perfect custom UI, consistent look across iOS and Android, or are targeting web and desktop alongside mobile. Best for design-heavy apps with complex custom animations.
Best when: You need maximum OS integration — HealthKit, Live Activities, AR depth APIs, hardware peripherals — or raw performance is critical. Best for apps where platform-specific capability is the core value proposition.
Delivery approach
Five stages from discovery to store — with a commitment that the app performs on real user devices before it ships, not after.
Week 1: define user journeys, platform choice rationale, AI feature prioritisation, and technical constraints (offline need, device targets, OS version floor). Output: feature list, architecture decision record, and a prioritised sprint plan.
Week 1–2: mobile-first UI design in Figma — platform-appropriate patterns (iOS Human Interface Guidelines or Material Design 3), AI interaction flows, and an interactive prototype user-tested with 5 real users before a line of code is written.
Week 2–5: navigation, authentication, state management, API integration, and offline-first data layer. AI features developed in parallel — on-device models integrated, streaming APIs wired, personalisation engine seeded.
Week 5–7: integrate all AI features into the live app experience. Performance profiling on real devices — not simulators. AI feature A/B testing against baseline. Automated E2E testing with Detox or Maestro across device matrix.
Week 7–8: App Store and Play Store submission — screenshots, metadata, privacy nutrition labels, age ratings, and review notes. Monitoring setup (Sentry, Firebase Crashlytics), analytics dashboards, and a post-launch optimisation plan for the first 30 days.
Client outcomes
Four organisations that replaced commodity mobile apps with intelligent applications — and the results that followed within the first quarter.
The Challenge
UK challenger bank with 1.4M active app users experiencing 28% monthly churn — users cited "the app doesn't understand me" in exit surveys. No personalisation, one-size-fits-all notifications, and a support experience that started with an FAQ chatbot users immediately tried to bypass.
What We Built
AI-powered mobile banking experience — on-device ML for spending pattern analysis (no data leaves the device), personalised insight cards surfaced at the right time, intelligent notification suppression based on user behaviour, and a React Native copilot integrated with Claude API that answers real questions about the user's own account.
UK challenger bank with 1.4M active app users experiencing 28% monthly churn — users cited "the app doesn't understand me" in exit surveys. No personalisation, one-size-fits-all notifications, and a support experience that started with an FAQ chatbot users immediately tried to bypass.
NHS Trust needing a patient-facing mobile application for wound care management — patients photographing wounds at home for remote clinician review. Previous solution was WhatsApp photos, creating DSPT compliance issues, no structured data, and clinician time wasted on triage of low-quality images.
Fashion retailer with 2.8M mobile users and a 3.4% product discovery rate — users browsing but not finding. No visual search, generic recommendations, and a search function that returned zero results for 23% of queries because users searched by style, not product name.
Industrial equipment manufacturer deploying field service engineers to 4,000 sites annually. Engineers using printed manuals and phone calls to the office for technical support. 3.2-hour average job resolution time, 18% first-visit failure rate, and no offline capability when sites had no connectivity.
Mobile engineering stack
smartphone users globally by 2025 — mobile is now the primary digital surface for most of the world, and the channel where AI-powered personalisation delivers the highest measurable ROI.
higher user retention for AI-personalised mobile apps — personalisation based on real behaviour, not demographic segments, is the single most effective lever for mobile engagement.
reduction in e-commerce returns when AR try-on is available in mobile — customers who visualise products in their own space return fewer, and buy more confidently.
reduction in field service resolution time when engineers have AI-assisted offline documentation on mobile — the impact of AI is highest where connectivity is lowest.
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Start with a free Mobile AI Opportunity Review — we'll look at your current app or your idea, identify the three AI features that would drive the most retention, and sketch an architecture that gets them to the App Store in weeks.