Questions, answered.

Founders and leadership teams at companies that have a real problem to solve — from early-stage startups to established businesses modernising how they operate.

A clear problem is more useful than a long feature list. A conversation can start with the workflow you want to improve, who uses it and what a better result would look like.

No. Rules, standard software or a better data process may be more reliable and cost-effective. Feasibility work should compare those options rather than assume AI is necessary.

No. Assessing and preparing your data is often part of the work. We'll tell you honestly what state it's in and what it will take to make it useful.

A first working prototype usually lands in weeks, not months. Full production builds vary by scope, and we agree milestones up front so there are no surprises.

Define access boundaries, permitted uses, retention and provider requirements before sharing data. Those decisions should be reflected in the scope and technical design.

Use representative examples and agreed success measures. Depending on the task, that can include answer quality, error rates, unsupported claims, response time and operating cost.

We offer ongoing support and improvement retainers, or a clean handover to your own team with full documentation — whichever suits you.

The handover plan should make that possible. Agree documentation, training, access, ownership and operational responsibilities as part of the engagement.

Start with goals and constraints, then define deliverables, milestones, responsibilities and acceptance criteria. Any change should be discussed before it becomes additional work.