We work best with NZ businesses past the startup phase, typically with 5 to 100 staff. Large enough to have real data and processes worth improving, and small enough that AI actually gets used rather than stuck in IT approval queues. We also take on contained AI projects with larger organisations where speed matters more than procurement process.
No, and that breadth is useful. The AI techniques that work in one industry often haven't been applied in another yet. Our team's background spans professional services, logistics, retail, healthcare operations, and financial services. If your sector is highly regulated, we'll tell you upfront what that means for your AI timeline and data handling.
It depends on the size and shape of the project, and we'll give you a realistic estimate once we've seen yours. As a rough guide, strategy and planning work takes weeks rather than months, and building and deploying a working solution takes longer depending on complexity and your data readiness. Ongoing engagements for monitoring, refinement, and enablement run month to month with no lock-in.
Most businesses aren't fully ready, and that's fine. It's what the Orient phase is for. The honest blockers are usually data quality and process clarity. If you can't describe a process consistently, AI can't learn it. If your data is in five disconnected spreadsheets, you'll need to fix that first. We'll tell you exactly where you stand and what needs to happen before a build makes sense.
No. We're not here to hand off code to your developers. We build it and we run it. That said, having someone internally who can be trained to own and maintain AI systems after we leave makes a meaningful difference to long-term outcomes. We'll identify who that person should be and build their capability as part of the engagement.
New Zealand's Privacy Act 2020 sets the floor, and we work above it. We don't send client data to third-party AI services without explicit agreement. We define data handling protocols before any project starts, and we build systems that are auditable. If you're in a regulated industry like health, finance, or legal, we scope compliance requirements upfront, not halfway through a build.
It depends on the scope, so we won't pretend there's a standard price. What we can promise is how we charge: every project is scoped and quoted upfront, we don't bill by the hour, and we don't run open-ended engagements. You'll know the cost before you commit to anything.
We agree on success metrics at the start of every engagement, not the end. Typical measures include time saved per process, error rate reduction, volume handled without additional headcount, or revenue impact from better decisions. Every project has a measurable target. If we're not hitting it, we say so and adjust before it becomes a problem.
This is more common than you'd think. We offer a structured recovery assessment: review what was built, diagnose why it's not working (usually data quality, scope creep, or poor integration), and give you an honest recommendation to fix it, rebuild it, or cut it. There's no benefit to us in recommending a rebuild if a fix is the right answer.
Most AI hype is written for enterprises with dedicated data teams and six-figure tooling budgets. We focus on what AI reliably does in real NZ businesses, with real data, in the next year. We won't pitch you a large language model to replace your customer service team if a simpler automated workflow solves 80% of the problem at 10% of the cost. The only measure is whether it works in your business.
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