Three ways towork with us.

This is the reason most clients find us. Hiring a senior engineer locally takes around eleven weeks between search, interviews and notice periods. This takes two.

They work inside your systems, not alongside them — your repository, your issue tracker, your stand-ups, your review queue. A written update goes out daily and working software is demonstrated weekly. Every pull request is reviewed by a second senior engineer of ours before it reaches you, so you get two engineers' judgement at one engineer's rate.

If it isn't working at the end of the trial, you stop — no notice period, no exit fee, and you keep everything that was built.

For work too large for one person and too urgent to hire for — a migration, a rebuild, or a date you have already committed to publicly.

The lead owns sequencing and is your single point of contact, so your product manager stops acting as a router between four people who each know a quarter of the problem.

Pods grow mid-engagement or reduce to a single engineer without renegotiating the relationship. Most of ours start at three and settle wherever the work actually needs.

Some teams don't need more hands. They need someone who has seen this failure mode before and can say so with authority.

A fractional lead sets review standards, arbitrates architecture, sits in your hiring loop, and writes the decisions down so they survive the person who made them.

It is the cheapest thing we sell and frequently the highest leverage, because it stops work being done that should never have started.

Scope, schedule and cost agreed line by line before work starts, and working software every week rather than a progress bar.

We make the boring decisions well — data model, type safety, tests, deployment, error handling — so the surface can afford to be remarkable. Handover documentation is written into the scope, not sold as an extra once you are already committed.

If the scope genuinely moves week to week, say so early and we will propose a pod instead. Fixed price only works when the thing being priced holds still.

Most AI projects fail in the same place: the demonstration is impressive, and then nobody can say whether it is right. We work in the opposite order.

The evaluation set is written at the same time as the feature — real questions from your domain, with answers approved by people who would know. Every change after that is measured against it, so accuracy is a number you can put in front of a board.

Every model call carries a cost limit, a latency limit and a defined fallback. Where the honest answer is a database query rather than a model, we will tell you — that conversation costs you nothing and saves a great deal.

Data pipelines, warehousing, deployment, observability, and the connected-device work that feeds them. The least glamorous thing we sell, and most often the actual problem.

You finish with runbooks your own team can follow — not a dependency on us. Where we do continue operating something, we say so explicitly and price it separately, rather than letting you become reliant by accident.

We will also tell you when your infrastructure is fine and the problem is elsewhere. That has happened more than once and it costs us a sale each time.

Nobody quotes a repair before looking. We spend five days producing a written assessment — what exists, what is dangerous, what it would cost to make safe — at a fixed fee agreed in advance. That document is yours whether or not you continue with us.

Then we stabilise before we improve: tests around whatever breaks most often, a deployment process worth trusting, and a written map of the system so the next person isn't starting blind.

We will usually argue against a full rewrite, and show you the arithmetic. Rewrites are how companies spend two years standing still.

We map how your team genuinely sells before touching the org — which usually means shadowing representatives rather than interviewing managers, because what people say they do and what they actually do differ in ways that matter.

The result is normally removing more than we add. Eight years of accumulated customisation is the most common thing standing between a sales team and their CRM.

Because we also build the products around it, integration is done by the same people — no handoff between a platform vendor and an engineering vendor, and no argument about whose fault it is when the sync fails.

Marketing teams are usually not short of tools. They are short of numbers they can defend.

We rebuild tracking around real product events rather than page views, reconcile it against your database, and restate historical reporting where it has been wrong — with the method documented, rather than quietly changing the figures and hoping nobody checks.

Then automation that fires on what customers actually did, and landing systems your marketing team can use without booking a developer for every campaign.