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Fixed-price or hourly — how we decide, and why it matters to you.

The pricing model isn't just admin — it changes who carries the risk of scope changing mid-project. Here's how we choose.

Fixed-price and hourly aren't interchangeable labels for the same work — they change who absorbs the cost when a project's scope shifts. Well-defined work goes fixed-price so you get budget certainty; genuinely exploratory work goes hourly so you're not paying a padded buffer for uncertainty that may never materialise. We recommend the model that reduces risk for your specific project, not the one that's easiest for us to bill.

We publish these because most of what's written about software delivery and AI is either pure hype or pure cynicism — this is just an honest account of how we actually work.

We're deliberately not the studio that quotes you a number and disappears until the deadline. Expect short, regular check-ins, staging access from early on, and a straight answer any time something turns out to be harder — or easier — than it looked at the start. If a piece of this isn't the right fit for your team, we'll say so directly rather than stretching the engagement to fill a quarter.

How we build it

01

Scope it honestly first

We won't quote fixed-price on something that isn't actually scoped yet — that's how projects blow their budget.

02

Match the model to the risk

Clear deliverables → fixed price. Open-ended exploration → hourly, with a budget ceiling you set.

03

Flag changes as they happen

A scope change mid-project gets its cost/timeline impact stated plainly, before we act on it.

Frequently asked

Yes — this reflects our real day-to-day process, not a marketing simplification. Ask us for specifics on any project and we'll walk you through it in as much detail as you want.

This is one of a small set of field notes we publish — get in touch and we're happy to talk through any of it in more depth, including specific numbers and examples.

The same principles apply regardless of size — a two-week landing page project gets the same review discipline as a multi-month platform build, just scaled to fit.

Who this is for

  • Founders and engineering leads thinking about how AI fits into their own delivery process
  • Anyone evaluating how we think about the work before reaching out
  • Teams comparing how different studios talk about AI versus how they actually use it

What you walk away with

  • An honest account of how the work actually happens, not a simplified pitch
  • Specific, concrete examples rather than abstractions

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