Application development services, from architecture to production

Architecture, UX and UI design, build, integration, QA, release, and post-launch support, across web applications, mobile apps, and the backend APIs behind both. In practice it means one team owns the interface, the APIs, the data model, and the deployment, so nothing falls between vendors.

It tracks surface area more than technology. A narrow internal tool is weeks. A platform with commerce, assessment, media, and an AI layer is months, staged so something usable lands early instead of everything landing at once. For scale: a multi-vertical learning platform we rewrote, 622 API routes and 143 mapped legacy tables, shipped in roughly 12 weeks with a two-person team.

Cost follows scope: how many screens, how many integrations, and how much of the data model already exists. We don't publish rate cards, because a number without a scope is guesswork. Bring us the workflow you want to replace and we'll come back with a phased plan and what each phase buys you.

Yes, and it's some of the strongest work we've done. We moved a business off an end-of-life PHP monolith as a strangler migration: the new stack ran alongside the old one against the same 143-table production database, cut over module by module, with no schema-altering migrations. Existing users kept their passwords, because login was built to accept the legacy hash format.

By matching the architecture to the actual constraint. Caching keyed on content rather than a fixed timer, so a cache clears when the data changes instead of on a schedule. Rate limiting held in Redis so it applies across instances. Background queues for work that doesn't belong on the request path. Multi-stage Docker builds with health-gated startup. Scale is a set of specific decisions, not a stack choice.

Yes. AI-native application development is part of the practice, not a separate service. We've shipped a five-stage LLM assessment pipeline on a public page, a RAG tutor grounded in each lesson's own documents, and AI-generated lesson material for instructors. Every model-returned record ID is re-checked against the database before a user sees it, and each stage logs its own token count and duration so AI cost stays measurable.

Yes. Production deployments ship with monitoring, alerting, and runbooks, and we stay engaged after launch to handle what real traffic reveals. Longer term we work as a technical partner rather than a one-off vendor, whether that means new feature work, dependency upgrades, or paying down debt we flagged during the build.

The IP in what we build is yours contractually, and we work under NDA when a project calls for it. Most of our delivered work stays anonymous for that reason: the case studies on this site describe the engineering problem and how we solved it, not the client.