Test before you build. We ship a clickable prototype, technical recommendations, a build roadmap, and a ballpark estimate — so engineering spend goes to ideas that have already earned it.
Prototyping pays for itself when the cost of building the wrong thing is high and the cost of finding out is low. Here are the situations where it makes the most impact.
There is a product in here somewhere. You want to test the riskiest assumption before committing months of engineering to the wrong shape.
A Figma file cannot tell you whether an LLM will actually answer the question. You need a prototype that talks to a real model and behaves like the thing.
Sales, product, and engineering are arguing in the abstract. A clickable artefact ends the meeting faster than another deck.
A working prototype during the pitch beats screenshots and roadmaps. It signals execution risk is lower than the deck suggests.
Research without an artefact gets opinions. Research with a prototype gets reactions to the actual product — the kind that change roadmap decisions.
Health, financial services, or government work needs feasibility tested early. A prototype surfaces compliance and integration risks before procurement does.
The right prototype depends on who has to see it. The same artefact rarely serves all three audiences well, so we pick a lane before we start.
Stakeholders, sales, and engineering need to see the same product. The prototype replaces the deck, ends the debate, and unlocks the budget.
Research participants need something to react to. A clickable prototype turns vague feedback into specific reactions tied to specific flows.
Fundraising rewards execution evidence. A working prototype during diligence shifts the conversation from "could you build this" to "how fast can you ship".
A productised engagement with named deliverables — not a Figma file thrown over the fence. The package is designed to make the next decision obvious.
Real flows, real navigation, real content. In your hands within weeks — ready for users, stakeholders, or investors to react to.
When the product depends on an LLM, the prototype runs against a real model — Claude, GPT, or whatever fits — not a screen of one.
Stack, architecture, and integration choices grounded in what we just built — not generic best-practice slides written before we knew the problem.
A sequenced plan from prototype to launched product, scoped against your team and the assumptions the prototype confirmed or broke.
Engineering effort and cost ranges based on the actual prototype — not the pre-discovery guess that sat in the proposal.
Privacy, audit trail, and compliance considered from the first sketch — useful when you build for health, financial services, or government across AU and NZ.
Five steps from question to packaged recommendation. Framing is non-negotiable — it is what stops the engagement from prototyping the wrong thing in high fidelity.
Narrow the question. Pick the riskiest assumption and the audience that has to see it tested before the next decision.
Establish flow and information architecture in low fidelity. Faster iteration, cheaper changes, fewer arguments about pixels.
Build the clickable artefact with real interactions, real data shapes, and working LLM calls where they carry the experience.
Put the prototype in front of the audience that will commission the next step. Capture what surfaces while the signal is fresh.
Wrap the prototype with technical recommendations, a build roadmap, and a ballpark estimate. You leave with a decision, not a Figma link.
Fidelity is a tool, not a target. We pick the lowest level that answers the question — higher fidelity costs more and surfaces less.
| Fidelity | What it is | When it is right |
|---|---|---|
| Paper | Hand drawings and rough sketches, iterated in a meeting. | The flow is unsettled and the team is still arguing about scope. |
| Wireframe | Greyscale screens with labels, no styling, clickable in places. | Information architecture is the question, not visual design. |
| Clickable | Interactive prototype with real navigation, content, and styling. | Real users or stakeholders need to react as if the product already exists. |
| Coded POC | Working code with real integrations — often an LLM or third-party API. | The riskiest assumption is technical, not user-facing. |
Three artefacts that get used interchangeably and shouldn't. Each one tests a different question, and picking the wrong one wastes the budget.
Will people use it?
Tests user-facing assumptions. Designed to be discarded once the question is answered.
Can it work?
Tests technical assumptions — usually one risky integration or capability, in isolation. Not a product.
Will people pay for it?
A real product launched to real users. Built to scale beyond the first cohort, not thrown away after testing.
A focused, opinionated stack — chosen because it builds prototypes fast and survives the path to production when the prototype earns it.
Four to six weeks is the typical shape. Smaller scopes — a single risky flow or a coded POC for one AI integration — can land in two to three. We commit to a duration up front so the engagement has a clear edge.
Pick the artefact that tests the riskiest assumption. If the risk is "will users understand this", build a prototype. If the risk is "can the model actually do this", build a POC. If you have evidence on both already and need real revenue and real users, you are ready for an MVP.
The prototype includes working LLM integrations — not screens of them. We wire it to a real model, with real prompts, against representative data. You see how it behaves under realistic inputs, not how it looks in a polished video.
A clickable prototype, technical recommendations, a build roadmap, and a ballpark estimate. Code and design files for everything we made. Enough material to make the next decision — keep building, change direction, or stop.
Clickable prototypes are built for speed and discardability — they are not the production codebase. Coded POCs are partially reusable depending on the stack. Either way, the build roadmap covers what gets carried forward and what gets rewritten, so there are no surprises later.
Yes. We build prototypes that hold up under live demo conditions and have shipped them into fundraising rounds. The roadmap and estimate are also useful artefacts for diligence — they show that what comes next is scoped, not hand-waved.
It does. Health, financial services, and government work in AU and NZ usually has a longer build runway, which makes prototyping more valuable, not less. We design with privacy, audit trail, and integration constraints in mind from the first sketch.
You do. All design files, code, prompts, and supporting material belong entirely to your organisation from day one.
Tell us where you're headed and we'll figure out the best way to get you there.