AI and Machine Learning
Retrieval, vector search and LLM features built to hold up under real traffic, not just in a demo.
Whichever line below brings you here, the same four engineers do the work. The stack underneath does not change either: TypeScript on Node, with NestJS on the server and React and Next.js in the browser, Flutter or React Native when it is a phone, and Python where the problem is machine learning or retrieval.
The split is by surface, not by seniority. A marketing site and a multi-tenant web application are different amounts of work, but there is no junior bench that the smaller one gets handed to, and no account manager sitting between you and whoever is writing it.
If you are not sure which of these your project is, it is probably more than one. Most of what we ship is a web application with a mobile app talking to the same backend, or an existing product with an AI feature inside it that has to survive real traffic rather than demo well. Describe what you are building and we will tell you which pieces it actually needs, including the ones you have not asked for and the ones you do not need yet.
Every engagement runs the same way: a discovery call, then a written PRD you approve before any code exists, then agile iterations and a UAT sign-off at the end. That process, and three systems we have already put into production, are described on the about page. What we do not take on is staff augmentation or WordPress work.
Retrieval, vector search and LLM features built to hold up under real traffic, not just in a demo.
Websites and web applications on React, Next.js and NestJS, built to stay fast as they grow.
Cross-platform and native applications for Android and iOS, built for performance and reach.
One codebase, both platforms. Flutter apps that look and behave natively on iOS and Android.
Polished applications for the Apple ecosystem, built to platform conventions.
Scalable, feature-rich Android applications built for the breadth of the Android ecosystem.