We add a specific piece of AI to the software your team already opens every morning — and tell you plainly when AI is the wrong tool for the job.
Most businesses asking about AI integration services in Australia don't need another AI product. They need one small piece of AI doing one specific job inside the systems they already run — the Xero file, the Cin7 stock data, the SharePoint folder of PDFs, the spreadsheet three people edit at once. That is the work: adding capability to what exists, rather than asking your team to move somewhere new.
The distinction is practical, not philosophical. A separate AI tool arrives with its own login, its own copy of your data and its own maintenance. It sits beside the business instead of inside it, and that is the version people stop opening after a month. AI that runs inside an existing workflow — flagging the invoice, drafting the reply, pre-filling the form — gets used because nobody has to remember to use it.
We will also tell you when AI is not the answer. A good share of what gets described to us as an AI problem turns out to be a rules problem or a data problem. If a decision follows a fixed policy, a rule is cheaper, faster and auditable: you want workflow automation, not a model. And if your data sits in four systems that disagree with each other, no model fixes that. It just becomes confidently wrong more often.
Drawn AI was started in 2024 by Liam and Jacob, out of backgrounds in retail operations management and mining operations. That shapes the work we do with multi-site retail businesses and mining contractors and services companies. We work Australia-wide from bases in Melbourne and Queensland, on a monthly subscription rather than a fixed-price project, and anything we host for you sits in Australian regions.
Anything that arrives as a stream and has to be sorted — support email, warranty claims, incoming site paperwork — can be read on arrival and sent to the right queue or person. The model reads it. The routing rules stay yours, visible and editable.
Turning a PDF invoice, remittance or delivery docket into structured fields your finance system will accept. This is the highest-return AI in most businesses, and it is covered properly on our document and knowledge intelligence page.
First drafts of quotes, customer replies, incident summaries and handover notes. A person still edits and sends. The value is in never starting from a blank page, not in removing the person from the loop.
Pointing at what looks wrong: a price that moved, a stocktake variance outside the normal range, a timesheet with impossible hours, a supplier invoice that does not match the purchase order. Flagging, not deciding.
A search-and-answer layer across your procedures, specs, contracts and past jobs — the shared drive nobody can find anything in. Answers cite the source document so the reader can check them.
Anything touching money, safety, employment or a customer commitment goes past a person. We build that review queue as part of the system: what is pending, who owns it, what the model suggested, and what got overridden.
Half a day with the people doing the work. We are looking for the specific task, how often it happens, what a mistake costs, and who currently catches it. If nothing clears that bar, we say so.
We map where the data lives and what condition it is in. This step regularly turns an AI project into a smaller data and measurement project first, which is a cheaper answer than the one you came for.
We agree what the model does, what it never does, and where a person signs off. Then we build against your real data rather than a demo set, and measure it on decisions your team has already made.
It goes into the systems you already run, through their APIs. The team's day looks much the same, with fewer manual steps in it.
Models drift, suppliers change formats, businesses change. We watch accuracy and override rates and tune from there. That is what the subscription pays for.
We work on a monthly subscription rather than a fixed project fee, with a three-month minimum term, monthly billing and 30 days' notice to cancel. The monthly figure depends on scope and how many systems are involved. It covers the build, the integration, hosting and ongoing tuning, so there is no separate maintenance bill arriving later.
Discovery takes a few hours of your people's time, spread across a week or two. After that most of the work happens away from them. Because the AI runs inside tools they already use, there is no new platform to learn — usually the change is a field that arrives pre-filled, or a queue that arrives pre-sorted.
Anything we host for you sits in Australian regions — Azure Melbourne and AWS Bedrock Melbourne. We scope what a model can see before we build, and it is normally far less than people expect. We do not train shared models on your data, and we do not push it through consumer chatbot products.
Then we tell you during discovery. Often the honest answer is a rule, a report or an integration rather than a model, because rules are cheaper to run and easier to audit. We would rather build the smaller correct thing than sell you the larger interesting one.
It depends on the task. Sorting into a handful of clear categories is usually strong. Pulling fields out of inconsistent documents is good but never perfect. We measure against your own historical decisions before go-live, tell you the number we get, and design the review step around what we find rather than what we hoped for.
Often, partly. If the same customer exists three times under three spellings, a model treats them as three customers. We would usually do a limited clean-up on the fields the AI touches rather than a full data project — enough to make the output trustworthy, not enough to stall the build.
A first conversation costs nothing and usually ends with a clearer idea of what is worth building — sometimes that answer is “not yet”, and we will say so.
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