Almost never. Most LLM integrations work on top of existing architecture – connecting to your APIs, databases, and workflows without a full rebuild. We map the integration points in the first week so you know exactly what's involved before we start.

We build evaluation and testing into the delivery process – not as an afterthought. That means defining what "good output" looks like for your use case, testing against real data, and setting up monitoring so you know when something drifts after launch.

We don't hand off and disappear. Every build includes handoff documentation your team can act on, and we offer ongoing retainers for teams that want continued support. If something breaks or drifts, we're set up to catch it and fix it.

We build compliance into the architecture – data residency, audit trails, access controls – not as a retrofit after a legal review flags the gap. For clients in regulated industries or operating under EU AI Act risk tiers, we address this in the scoping phase, before a line of code is written.

The Sprint exists because most teams arrive with an idea, not a spec. It de-risks the build: you get a working POC, an integration map, and an ROI estimate before committing to a full engagement. If you already have a validated concept and a clear brief, we can scope a build directly – we'll tell you which makes more sense after the first call.

Our AI product development timeline depends on the scope and complexity of your project. A single AI feature or integration can go from brief to production in a matter of weeks, while a full AI-native MVP typically takes longer, depending on the number of features, data readiness, and integration requirements. We work in short cycles so you start seeing working software early, rather than waiting for a single final release.