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How we build AI-native software: from workflow to working tool.
No discovery phases that outlive their budget, no 200-page specs. Here's the actual process — five steps, honestly described, for NZ and Australian operations businesses.
How does an AI-native build actually work?
We map one painful workflow — not a big system. You see a working prototype in days, react to it, and shape it. Together we decide where humans must approve before anything happens. The tool is wired into your existing system of record, and because AI-native software is cheap to change, it keeps evolving after go-live instead of freezing on day one.
The process, step by step
Map one painful workflow
Not a big-bang system. We sit with the person who actually does the job and map one workflow end to end — inputs, decisions, exceptions, handoffs. Sharp edges, small scope.
Working prototype in days, not months
You react to a tool, not a spec document. This is the economics YC describes — "AI has collapsed the cost of producing software by 10–100x" (Jared Friedman, Requests for Startups) — applied to your workflow: prototyping is now cheap enough to do first.
Design the approval points
Together we decide which actions run automatically and which wait for sign-off. The rule: AI drafts, humans approve wherever an error would be costly — ledger postings, customer messages, stock changes.
Integrate with the system of record
The tool reads from and writes back to your platform — Unleashed, Cin7, NetSuite or Odoo — through its API. No parallel database quietly becoming a second version of the truth.
Evolve continuously
AI-native software is cheap to change, so it tracks your business instead of freezing at go-live. The adjustment that would be a change-request negotiation on a traditional build is a routine tweak here.
Why prototype-first works: nobody can tell from a document whether a tool fits how they actually work. Ten minutes using a rough version surfaces more truth than ten meetings about a spec — and when prototypes cost days instead of months, showing beats describing.
What this costs
The honest answer: it depends on the scope of the workflow, so we won't publish a standard price and pretend precision we don't have. What we can say plainly:
- The baseline has moved. Small, focused tools are now a fraction of the cost of traditional custom builds — that's the 10–100× collapse YC describes, and it's why workflows that never justified bespoke software now do.
- Scope before quote. The mapping session defines exactly what's being built; you get a clear quote for that defined scope before anything is built.
- Small scope, small bill. Because each build covers one workflow, you're never funding a monolith to get the one piece you wanted.
What we won't do: quote a number before mapping the workflow, promise an ROI percentage, or hide ongoing costs. If a workflow doesn't justify a build — because it's rare, or a platform feature already covers it — we'll say so and point you at the platform answer instead.
Start small: one workflow, proven, then expand
Prove
Cap the risk
One workflow means the downside is capped and the result is visible in weeks. You judge the approach on a working tool your team actually uses.
Expand
Grow on evidence
The second tool gets built because the first one worked — not because a roadmap said so. Each expansion is a decision you make with results in hand.
Exit
Stop cleanly
If it doesn't fit, you stop. A small build you walk away from is a cheap lesson; a big-bang programme you walk away from is a write-off. Your platform is untouched either way.
Frequently asked questions
How long does an AI-native build take?
A working prototype of a single workflow typically exists within days, and a production tool within weeks — because we scope to one workflow at a time, not a big-bang system. The mapping session at the start is where the timeline for your specific workflow gets set.
What does an AI-native build cost?
It depends on the scope of the workflow, so we do not publish standard prices. What has changed is the baseline: Y Combinator estimates AI has collapsed the cost of producing software by 10 to 100 times, and small focused tools now cost a fraction of a traditional custom build. You get a clear quote for a defined scope before anything is built.
Do we have to commit to a big project up front?
No — the opposite is the point. You commit to one workflow. If the tool proves itself, you expand to the next workflow on evidence rather than faith. If it does not fit, you have lost a small build, not a transformation programme.
What happens after the tool goes live?
It keeps evolving. AI-native software is cheap to change, so adjustments that would be a change-request negotiation on a traditional build are routine — the tool is expected to track your business as it changes, not freeze on go-live day.
Ready to map your first workflow?
Bring the most painful one. One session and you'll know what a build would involve, what it would cost for that scope — and whether it's worth doing at all.