Mobile app modernisation

A structured assessment guides the decision. We measure current stability, crash rates, performance hotspots, and dependency risks. Map the business‑critical journeys and estimate the cost of change. If most issues are localised, refactoring and selective re‑architecture pay back fastest. If the runtime, database, or hosting platform is the bottleneck, re‑platforming to a modern, observable stack is often best. A full rebuild is only for cases where key issues stop progress. These can include an unmaintainable monolith, outdated frameworks, or security risks. We model effort, risk, and expected gains (performance, reliability, development speed) to recommend the smallest viable change that delivers the required outcomes. This approach reduces technical debt while keeping velocity high.

We start with telemetry to find the real bottlenecks - cold starts, main‑thread blocks, chatty APIs, oversized images, or unindexed queries. Fixes target these root causes: lazy‑load non‑critical modules, batch network calls, cache smartly with sensible TTLs, compress and resize media, and enable HTTP/2/3. On device, we offload heavy work to background threads, schedule tasks when on Wi‑Fi/charging where appropriate, and add offline support to reduce retries. Server‑side, we tune queries, add pagination, and use CDNs for static assets. We lock in improvements with budgets and alerts, like startup time, frame stability, and payload size. This way, the app feels fast while also respecting battery and data limits.

Security is integrated into delivery. We adopt strong authentication (including biometrics where relevant) and least‑privilege authorisation. The central team manages secrets; they encrypt data in transit and at rest; and they maintain audit trails for sensitive actions. Pipelines enforce policy‑as‑code with dependency scanning, SAST/DAST, and container/image checks. We schedule regular penetration testing and review third‑party SDKs for data access. Compliance (privacy, consent, retention) is documented and tested, with clear user notices. Observability and anomaly detection help us respond quickly, while playbooks shorten incident MTTR. This keeps posture strong as features and integrations grow.

Discovery and planning usually take 2-4 weeks, leading into phased delivery. A focused refactor takes about 6 to 10 weeks. In contrast, broader re-platforming or cloud migration usually lasts 3 to 6 months. This depends on factors like integrations, data migrations, and non-functional requirements. Key drivers are integration access and quality, complexity of the codebase, test coverage, team availability, and the depth of performance, accessibility, and security work. We time-box outcomes for each phase and deliver in increments. This way, benefits come early. For instance, we see performance and crash-rate improvements before making bigger architectural changes. Transparent metrics and demos reduce uncertainty and help stakeholders make informed trade‑offs.

We maintain a compatibility matrix and upgrade in small, reversible steps. Feature flags isolate risky changes; canary and staged rollouts limit blast radius. Automated tests cover critical journeys, including background tasks, notifications, and permissions. We replace deprecated APIs, review platform behaviour changes, and validate on a realistic device and OS version set, including low‑end hardware. Third‑party libraries are audited for licence and security concerns. Monitoring (crash‑free sessions, ANRs, latency) is watched closely post‑release. This method keeps the store pipeline moving while protecting user experience.

We set baselines and target metrics before work begins. Common signals include performance (cold start, frame stability, API latency), reliability (crash rate, ANR rate), user experience (task completion time, retention, satisfaction), and delivery velocity (lead time, change failure rate). Commercial outcomes may include conversion, average order value, or support ticket reductions. We attribute improvements to releases where possible and track cohort behaviour to avoid false positives. The aim is a measurable lift in user satisfaction and business KPIs, paired with a lower cost‑to‑change thanks to improved architecture, tests, and automation.

Yes, and the migration sequence is designed around your release calendar from the start. Stable, versioned API contracts and clear module boundaries let the modernisation track and the feature track work in the same codebase without blocking each other, and feature flags keep both independently releasable. We agree which areas are frozen during their migration window and keep those windows short. There is a real coordination cost during the heaviest phases, so we plan it explicitly with your leads rather than letting the two tracks discover each other in merge conflicts.

No. Protecting the installed base is a design constraint from the first phase, not a launch-week scramble. The app keeps its store listing, so ratings, reviews and the install base carry over; updates ship as ordinary releases through the same pipeline. Local data, preferences and sessions are migrated in place on first launch of each new version, and the migration code is tested against real data snapshots before rollout. If a security improvement genuinely requires re-authentication, it is planned, communicated in-app and measured - never discovered by users as a surprise logout.

Yes - modernisation engagements often start exactly there. The audit is designed to work from the artefacts that actually exist: the repository and its history, build configuration, crash reports, store analytics and the running app itself. From these we reconstruct the architecture picture, map the dependencies and document as we go, so the knowledge gap closes as a side effect of the work. Access to the original developers helps and shortens discovery, but it is not a precondition; an unowned codebase is a reason to modernise, not a barrier to starting.