Build vs buy for AI: when off-the-shelf is enough

We tell founders this more often than you might expect: you may not need us to build that. Plenty of AI needs are well served by existing tools. Here is how we decide whether to buy, customise or build.

Buy when the job is generic

Meeting notes, generic writing help, standard customer support for common questions and internal search across popular tools are crowded categories. Mature products exist, and your differentiation is unlikely to come from rebuilding them.

Customise when the workflow is yours

If an off-the-shelf tool covers most of the job but your data, tone or approval steps are specific, configuration and light integration usually win. Automation platforms and model APIs make this fast.

Build when AI is the product

• The AI experience is what customers pay you for.
• Your proprietary data or workflow is the advantage.
• Off-the-shelf tools cannot meet your quality, latency, privacy or cost requirements.
• You need deep integration inside your own app's user experience.

Watch the hidden costs

Bought tools add per-seat fees and data-sharing questions; built systems add maintenance and model costs. Compare the total cost over two years, including the people needed to run each option.

Our approach

We start every AI engagement by asking whether something already exists that solves the problem well enough. When building is the right call, a four-week AI Pilot proves the value with your real data before you invest further.