How much does AI product development cost?

It's one of the first questions every team asks us, and it's also the hardest to answer honestly with a single number. AI product development doesn't have a rate card the way, say, a website rebuild does - the cost is driven almost entirely by which engagement model fits where you are, not by a day rate multiplied by a headcount.

Why there's no single answer

A discovery workshop that aligns leadership on where AI can create value costs a fraction of building and shipping a production agent system. A proof of concept that validates one use case with sample data costs a fraction of an embedded squad delivering a full product over several months. Anyone who quotes you a flat number before understanding which of those you actually need is guessing.

The more useful question isn't 'what does AI product development cost' - it's 'what's the smallest, cheapest step that tells us whether this is worth a bigger investment?'

The three engagement models, and what drives their cost

We structure AI work into three stages, and cost scales with each one:

• Discovery workshops (an AI Launchpad, in our case) - a fixed-scope, few-week engagement to align stakeholders and identify the highest-value opportunities. Lowest cost, lowest risk, and often the step teams skip when they shouldn't.
• Proofs of concept - a working technical prototype that tests feasibility and real-world impact with sample data, before you commit to a full build. Mid-cost, and the stage most likely to save you from an expensive mistake.
• Embedded product or design squads - cross-functional teams of AI engineers, product managers, and designers delivering a full production build. This is where most of the budget goes, and it should only start once discovery and proof of concept have de-risked the bet.

Skip straight to the third stage without the first two and you're paying full build cost to find out things a workshop or prototype would have told you for a tenth of the price.

What actually moves the number, within each stage

Once you're in a given stage, a handful of factors drive the real cost more than anything else: how much of your data and systems the AI needs to integrate with, whether you're automating an existing workflow or designing a new one from scratch, how much governance and testing the use case demands, and how senior a team the work requires. A customer-facing agent handling real transactions needs a different level of rigour - and cost - than an internal tool automating a reporting task.

This is also where engagement model matters as much as scope. A fixed day-rate contractor and an embedded senior squad can quote similar headline rates and produce very different total costs, because the squad ships working software faster and needs less rework. Cheaper per day isn't the same as cheaper per outcome.

How we scope it

We don't lead with a rate card because it would be dishonest to pretend one number covers a Launchpad workshop, a proof of concept, and a full AI Product Squad build equally well. Instead, we scope from wherever you are: a workshop if you're still working out where AI creates value, a proof of concept if you have a specific use case and need to validate it before committing budget, or an embedded squad if you've already done that work and are ready to build and ship.

If you want a straight answer on what your specific project would cost, that's a conversation, not a quiz on our pricing page - get in touch and we'll scope it properly, starting with whichever stage actually fits where you are.