Nine disciplines, one delivery team

Most of what we build is a web application that someone's business runs on: a dashboard their operations team lives in, a customer portal, a booking or billing system that has to reconcile to the cent.

We build these full-stack, which means one team owns the interface, the API and the data model. There is no handover point where the front end blames the back end for a rounding error.

Flutter covers most cases: one codebase, both platforms, native performance, and a single team maintaining it. We reach for Swift or Kotlin when a project genuinely needs platform-specific behaviour — background location, deep hardware access, or an SDK that only ships native.

If your users work in vans, warehouses or on sites, we design offline-first from the start. Sync conflicts are a product decision, not an implementation detail, and we will ask you how you want them resolved.

We do not add a chat box to a product and call it an AI strategy. Every AI feature we ship starts with a task a person currently does by hand, a measurement of how long it takes, and a target.

In practice that has meant OCR pipelines for scanned paperwork, extraction from unstructured documents into a validated schema, and assistants that call your existing functions instead of inventing answers. Where the model can be wrong, the interface shows its source and lets a human correct it.

Deployment should be boring. We set up containerised builds, a pipeline that runs the tests, environment parity between staging and production, and a rollback that has actually been rehearsed rather than merely documented.

Monitoring is part of the work, not an add-on. You get alerting that fires on symptoms your users would notice, not on every CPU spike at three in the morning.

Design runs in the same sprints as engineering. Screens are drawn against real content and real edge cases — the longest client name, the empty state, the row with fourteen line items — because those are the screens that break a layout in production.

For anything that will keep growing, we build a small design system first: tokens, type scale, a component set. It is cheaper than restyling forty screens in year two.

Sometimes the useful engagement is two weeks, not six months: a review of an existing codebase before you acquire it, a second opinion on an architecture, a decision about whether to rebuild or refactor.

We will tell you when off-the-shelf software solves your problem for a fraction of the cost of a custom build. It has lost us work and won us clients.

We build and run stores on Shopify and Shopify Plus, WooCommerce, and headless setups where the catalogue lives in a commerce engine and the front end is ours. Theme work, custom apps, subscriptions, multi-currency, multi-warehouse stock.

Most of the money is not in the homepage. It is in the migration that keeps your URLs and rankings, the feed that keeps stock honest across channels, and a checkout that does not lose people on step two. That is where we spend the time.

We run paid acquisition on Google, Meta and TikTok, and the email and automation programme that turns those visits into repeat orders. Because we also build the product, the tracking is wired properly: server-side events, clean attribution, no guessing which channel paid for itself.

Reporting is one page: spend, revenue, contribution margin, and what we are changing next week. No vanity dashboards.

An agent is only useful when it can act. We build agents that hold a real conversation on the phone, on WhatsApp or in a chat window, then take the action behind it: create the booking, move the order, reschedule the driver, issue the refund — through your existing APIs, with the same permissions a member of staff would have.

Every agent runs against a written playbook: what it may decide alone, what needs confirmation, and when it hands over to a human with the full transcript. Actions are logged, costs are capped per conversation, and the failure mode is always a warm handover, never a made-up answer.