AI products built to actually ship.
Custom applications built around language models, your data and the workflow your team already uses. Not a demo. A real product with evals, guardrails and a human in the loop.
The problem
Most AI projects never leave the demo stage.
A chatbot that impresses in a meeting and falls apart in front of a real customer, a tool that invents a policy that doesn’t exist, a pilot that never secures budget for a second phase because nobody planned for one. These aren’t really AI problems at all, they’re engineering problems, and they are the reason most AI initiatives stall somewhere before production.
Language models are genuinely capable at retrieval, drafting, classification and structured extraction. They are also confidently wrong on occasion, and a product that doesn’t account for that will lose a customer’s trust quickly. We build the unglamorous infrastructure underneath, evals, guardrails, monitoring, cost control, so the impressive part keeps holding up once real traffic arrives.
That’s the same discipline behind our custom WordPress builds: working software over polished decks, with a named engineer accountable for the outcome, not just the demo.
There’s also a simpler, more practical question buried under all of this: which model do you even use? New ones launch every few months, each with different strengths, different costs and different limits. Picking one and locking yourself in early is a common, expensive mistake. We stay close to the major providers and choose per job, so you’re not stuck maintaining a decision that made sense eighteen months ago and doesn’t any more.
What you get
A real system, not a proof of concept.
Retrieval over your data
Your documents, policies or product catalogue, indexed and searched properly, so that every answer is grounded in what is demonstrably true for your business.
Structured outputs and tool use
The model returns data your systems can act on, and can call the tools it needs, your CRM, a database, an API, with permissions you control.
Evals before launch
A scored test set of real questions and genuine edge cases, run before anything ships, so you understand how the system behaves before your customers encounter it.
Guardrails and fallback
Explicit rules for what the system won’t do, plus a clean path to ‘I don’t know’ or a human handoff instead of a confidently incorrect answer.
Observability
Logging and dashboards showing what is being asked, what is being answered and where the system is going wrong, described in plain language rather than metrics.
Cost control
Model choice, caching and prompt design tuned so running costs stay predictable as usage grows, not a surprise on next month’s bill.
Process
How a build runs.
Scoping
One to two weeks, during which we map the use case, confirm the data is workable, and agree a fixed price and a fixed scope before anything gets built.
First version
Four to eight weeks for a working system covering the core use case, tested against real examples, not a scripted demo.
Review and harden
We run the evaluation suite, tighten the guardrails, and repair whatever genuine usage surfaces that the first version failed to anticipate.
Launch and iterate
You go live with monitoring already in place, then we adjust around what actual users do, which is invariably different from whatever we planned for.
Why Maku
Engineers who ship, not a slide deck.
We build AI products and do deep WordPress engineering under the same roof. That combination is rare, and it means we can put an AI feature inside a real website, not hand you a standalone tool that never gets used. Have a look at what we build in AI agents if the work involves taking actions in your systems, not just answering questions.
- Fixed-scope, fixed-price pilots, so you know the number before committing to anything.
- You own the code and the prompts outright. No platform lock-in, and no exit fee if you ever want to move.
- We build AI products with AI in the loop, which is a fair test of whether we believe our own pitch. An engineer still decides what ships.
- Honest about failure modes. We will tell you where a model is likely to struggle before you discover it from a disappointed customer.
- Not sure yet whether a full build is the right first step? Our AI strategy engagement scopes the use case and the cost before you commit to either.
Do you train your own models, or use existing ones?
We use current models from the established providers, open-weight ones included, and pick whichever fits the job. We don’t train foundation models. Most business problems don’t need one. They need the right model, good data and a system built around it properly.
What does AI development cost in Australia?
It depends on scope, so we work that out first rather than quoting blind or publishing a figure. Every pilot is fixed-scope and fixed-price before we start, which means you know the number before you commit to anything. Send us the use case and we’ll scope it.
How long does a build take?
A working first version usually takes four to eight weeks, depending on how much data prep and integration work is involved. We treat that first version as a start, not a finish, and plan for a few rounds of change once real users touch it.
Is this just vibe coding?
No. We use AI tools to move faster, but an engineer designs the system, writes the evals, reviews every output path and owns what ships. AI drafts, a human decides. That line doesn’t move.
Does a human review what the AI writes or does?
Yes, at two levels. We review the code and prompts as we build, and we design the product so a human can review or approve what the AI produces for your customers, wherever the stakes are high enough to need it.
What data do you need from us?
Usually a sample of the content, documents or records the system needs to work from, plus access to whatever it needs to connect to. We’ll tell you exactly what’s needed and why during scoping, before any contract is signed.
What happens to our data, and who sees it?
Your data stays yours. We use it to build and test your system, we don’t use it to train anyone’s foundation models, and we’ll set out exactly how it’s stored and processed in the project agreement.
What happens after launch?
You own the code and the prompts, so you can run it yourselves, hand it to another team, or ask us to keep improving it. Most clients start with a support arrangement while usage patterns settle, then move to lighter ongoing care.