One team across the whole stack

Web apps, APIs, LLM features and agents are no longer separate projects with separate teams; a product needs all of them and they have to agree with each other. One team builds the whole thing, in your repo and your cadence, with no account-management layer in between.

Most AI programmes fail on the choice of problem, not the model. We audit what you actually do, what it costs and where the time goes, then cost the options honestly and tell you which are worth building, including the ones that are not. Every candidate comes back with a cost, a latency budget and an achievable accuracy, estimated before anything is built, so the trade-offs are visible while they are still cheap to change. A meaningful share of these end with a recommendation not to build, which is the answer that saves the most money.

Journeys, states and edge cases designed first, including empty, loading, error and permission states, because those are where products feel broken and where engineering otherwise has to improvise. Interfaces for AI get particular attention: showing where an answer came from, and giving someone a way to disagree with it, is the difference between a feature people trust and one they abandon after a single wrong reply. You get a design system small enough to maintain and specific enough to build from, not a deck of concepts.

iOS and Android apps built by people who have shipped them through review before, including the parts that catch teams out: signing, staged rollout, privacy declarations and the first submission. Native or cross-platform is a trade-off we make per project and explain, not a preference we bring with us. Offline behaviour is designed rather than discovered, because a phone loses signal and what happens next is a product decision. On-device AI goes in where latency, cost or privacy genuinely justify it, and stays out where it does not.

Cloud that is sized for the traffic you actually have, described entirely in code, and cheap enough that nobody dreads the invoice. Most of the estates we inherit are not badly built; they were sized for a launch that never arrived and never revisited afterwards. We map what runs, what it costs and what breaks it, then automate the release path until deploying is the least interesting thing that happens in a week. Everything lives in your own tenancy, so there is nothing to migrate when we are done.

Pipelines that fail loudly, a warehouse modelled on how the business actually thinks, and lineage you can follow from a dashboard back to the row it came from. Data incidents are rarely outages. They are silent degradations somebody notices a month later in a number that looks slightly off, which is why checks run with the pipeline rather than beside it. This is also the practice every AI feature quietly depends on: retrieval is only ever as good as what it retrieves over.

Old system to new, without the big-bang cutover that concentrates every risk on a single weekend. We map what runs today and what depends on it, then run the new path beside the old one and move traffic a slice at a time, with rollback available throughout. Equivalence is proven by shadow-running against real production traffic rather than by a testing phase everyone hopes was thorough enough. Sometimes the honest recommendation is to leave a system alone, and where that is true we will say so.