App development
Custom Android, iOS, and web apps designed around the workflow, with attention to UI, UX, user journeys, and interface mocks used to align style, flow, and usability.
Apps that solve real business problems.
Development of Android, iOS, and web apps, with supporting backend APIs where needed. Clear scope, direct communication, and handoff the client fully owns.
A methodology that turns broad ideas and goals into aligned prototypes, detailed technical proposals, and production delivery without lock-in.
Capabilities
Backend APIs
Authentication, integrations, data models, content pipelines, and supporting services that keep the product coherent and the app reliable.
Prototyping & technical scoping
Early examples, fast prototypes, and detailed technical proposals used to align expectations before implementation commits time and budget.
Delivery & handoff
Implementation with clear communication, predictable milestones, and final delivery in which code, credentials, and operational control remain with the client.
Projects Delivered
Language Learning Platform
Kotlin Multiplatform apps for Android and iOS, a React web application, and backend APIs for story delivery, dictionary lookups, audio playback, progress tracking, and offline downloads. Included multilingual content models, processing pipelines, and read-along audio alignment.
Agricultural Machinery Control App
A replacement mobile app for agricultural machinery control, built after reverse-engineering a proprietary Bluetooth protocol to remove vendor dependence. Added role-based workflows, historical usage visibility, and hardware integration for field operation.
Telemetry Healthcheck Automation System
A Kotlin backend that aggregated telemetry data from external APIs, identified inactive or malfunctioning devices, and automated daily reporting. Replaced manual monitoring and reduced recurring operational work.
Robotics Monitoring and Control App
An Android application for monitoring and controlling agricultural robotics, with cloud connectivity for data storage and integrations with LiDAR and machine learning outputs to support safer autonomous behaviour.
Machine Learning Anomaly Detection Pipeline
An LSTM-based pipeline for multivariate time-series anomaly classification, including data preparation, model training, and evaluation over more than 1 GB of industrial data.
Development Process
01
Understand
Clarify the business problem, the users, the operational constraints, and what a successful outcome actually looks like.
02
Prototype
Use examples or working prototypes to align expectations early and reduce ambiguity before deeper implementation begins.
03
Specify
Turn the agreed direction into a written proposal with scope, delivery approach, technical choices, and clear boundaries.
04
Build
Implement with direct communication, visible progress, and regular alignment as technical details become concrete.
05
Handoff
Ship the product with documentation, code, credentials, and infrastructure access handed over cleanly, without unnecessary dependency or lock-in.
Contact
For hiring conversations, referrals, or product discussions, contact starts here.