A four-week
method.
How an engagement actually proceeds, from the first conversation to the day we hand the system over to your team. The same shape for an audit as for a sprint, scaled to fit the work.
- 01 Week 1
Listen.
Before we propose anything, we sit with the people doing the work: the partner, the office manager, the senior associate, the front-desk lead. We watch the tabs they keep open, the documents they print, the spreadsheets they version-name. The aim is to find the friction your team has stopped noticing because they live with it daily.
- 02 Week 2
Diagnose.
We map the workflow as it is, then identify three places software could remove a meaningful share of the manual work. Each candidate is costed, sequenced, and scored against the metric that matters to your firm: hours saved, errors avoided, or time-to-respond. You receive this as a written brief, not a slide deck.
- 03 Weeks 3–4
Build.
A working version exists by the end of week three. We deploy it to a staging environment your team can poke at, with realistic data. Week four is hardening: the boring work of edge cases, error handling, audit logs, and the runbook that will keep the system maintainable after we are gone.
- 04 After
Hand over.
We hold a 90-minute handover with whoever will own the system internally. They receive the codebase, the documentation, the runbook, and a list of the things we deliberately did not build (along with why). For 30 days afterwards we are on call for questions and refinements at no additional cost.
Aesthetics in code. The constraints we impose on ourselves so the work survives us.
- 01
Build for the person who inherits this in two years.
Every line of code and every architectural choice should be legible to a developer who joins your firm long after we have left. Cleverness is a liability when nobody is around to maintain it.
- 02
Latency and reliability are product decisions.
A tool that is fast and predictable will be used. A tool that is slow and occasionally wrong will be abandoned, no matter how impressive its peak capability. We agree the latency budget and reliability target before architecture, not after.
- 03
Document what we did not do.
Every engagement ships with a list of decisions we made and trade-offs we accepted. The future maintainer should not have to reconstruct our reasoning from commit messages.
- 04
Default to the boring choice.
Postgres before MongoDB. Server-rendered HTML before single-page applications. Cron jobs before message queues. We reach for novelty only when the workload genuinely demands it, which in operational software is less often than the industry pretends.
We use language models where they demonstrably improve accuracy or speed, and deterministic code where they don't.
A meaningful share of the work that arrives at the studio asks the same question in different forms: where should we be using AI? Our answer is shaped by what we have seen fail. Document classification, intake triage, summarisation across long records, retrieval over a firm's own knowledge: these benefit from language models when paired with evaluation harnesses and human sign-off. Conflict checks, arithmetic, deadline calculation, regulatory rules: these belong in deterministic code, even when an LLM can do them most of the time.
We will tell you which is which for your particular workflow. We will not ship language-model features for the sake of saying we did. Where AI is used, it ships with an evaluation suite the firm can run before each deployment, and a human-in-the-loop step the partnership controls.
"The work is finished when the system runs quietly enough that nobody on your team has reason to think about it. Until then, we are still in the middle of the engagement."
A method, properly applied,
looks unremarkable.
That is the goal. If your firm is ready for that kind of unremarkable, we should talk.