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Pricing

We price by engagement, not by a published rate card. Every project starts from what you actually need to ship, and we are transparent about what moves the number. We have built production software since 1998 -- the estimate reflects the work, not a sales target.

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How we price

We do not publish a rate card because the rate card is not the useful part. Two projects at the same notional day rate can differ by an order of magnitude in real cost depending on scope, data, and how many systems the work has to touch. So instead of a price list, we scope each engagement to what it actually requires and tell you the number and the assumptions behind it. AI that ships, not AI that demos, and pricing that matches.

Engagement models

Most work falls into one of a few shapes. Each is scoped and priced on its own terms; many clients start with an assessment and grow from there.

AI readiness assessment

A fixed-scope diagnostic. We look at your data, systems, and goals and tell you honestly where AI will pay off and where it will not. The cleanest place to start if you are not sure what is worth building.

AI strategy and training

Advisory and enablement work: roadmaps, architecture decisions, and hands-on training for your team. Usually scoped as a defined engagement with clear deliverables rather than an open-ended retainer.

AI implementation

We build and integrate the system: data pipelines, evaluation, and the production plumbing real software needs. Priced to the scope of what ships, not the size of the slide deck.

Build engagements

Custom software and application development, from greenfield products to modernizing systems that still run the business. Scoped per project once we understand the surface area and the constraints.

Team augmentation

Senior engineers embedded alongside your team for a defined period. Priced by the people and the duration, with the same senior-by-default standard as the rest of our work.

What drives cost

When we scope an engagement, three things move the estimate more than anything else. Knowing where you stand on each is the fastest way to a realistic number.

  • Scope

    What actually has to ship, and how certain it is. A tightly defined build with a clear definition of done costs less to estimate and less to deliver than an open-ended exploration. We would rather narrow the first engagement than pad it.

  • Data readiness

    Whether your data is accessible, clean, and structured enough to build on. The unglamorous data work is often where AI projects succeed or stall, and it is usually the single biggest swing in effort between two otherwise similar engagements.

  • Integration surface

    How many systems the work has to touch and how cooperative they are. A self-contained tool is straightforward; something that has to sit inside legacy systems, auth, and existing workflows carries more integration and testing work.

The honest version: the more defined the scope and the more ready your data, the tighter and lower the estimate. When something is uncertain, we will say so and propose a smaller first engagement to settle it before committing to the larger build.

Want a number for your project?

Tell us what you are trying to build and where your data and systems stand today. We will scope it honestly and tell you what it takes -- including when the right answer is a smaller first step.

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