You don't have an AI team. You have people answering the same questions all day and retyping the same documents, while the answer sits somewhere in a system you already own. We build the thing that does that work, show you it runs, and hand it over.
Models & platforms we build on
AI is only useful if it stays up and keeps your data safe. One team handles all three, so nobody gets to point at the other two when something breaks.
AI put to work on the jobs taking up your team's time: answering the questions customers and staff ask all day, turning documents into data you can use. We measure it for accuracy and cost, then run it in production.
AI needs somewhere reliable to run. We build cloud on AWS, Azure or Cloudflare that grows without the bill running away, written as code and documented so it never lives in one person's head.
Putting AI near your data raises hard questions. We review what you have, decide what the model is allowed to see, and do the work an auditor will ask for. You get the list in the order we would fix it.
Most AI advice lists everything it can do. This is our read on the work a business runs day to day: how much time it eats, how ready the data behind it is, and where we'd start. Some rows say don't.
| Business function | Time it eats | How ready your data is | Where we'd start |
|---|---|---|---|
| Customer & staff questions | Time it eats: 5 of 5, most of a role. | How ready your data is: 4 of 5, mostly there. | An assistant over the documents you already have, escalating to a person when it isn't sure. The fastest honest win in most businesses. |
| Document processing | Time it eats: 5 of 5, most of a role. | How ready your data is: 3 of 5, workable. | Pull the fields out and push them into the systems you already run. We sample-check batches before anyone trusts the output. |
| Quoting & proposals | Time it eats: 4 of 5, a lot. | How ready your data is: 3 of 5, workable. | Draft from your own past quotes so it sounds like you. A person still prices it and a person still signs it. |
| Compliance evidence Human sign-off | Time it eats: 4 of 5, a lot. | How ready your data is: 2 of 5, patchy. | Gathering and formatting the evidence, yes. Deciding whether you comply, no. That stays with a person who carries the accountability. |
| Reporting & reconciliation | Time it eats: 3 of 5, a real slice. | How ready your data is: 4 of 5, mostly there. | Most of the time this is a data-plumbing job in an AI costume. We'd fix the pipeline first, then see what is left worth automating. |
| Onboarding & internal knowledge Not yet | Time it eats: 3 of 5, a real slice. | How ready your data is: 2 of 5, patchy. | There is nothing to search until the knowledge is written down. Write it down first; that alone solves most of the problem. |
| Rostering & scheduling We'd say no | Time it eats: 3 of 5, a real slice. | How ready your data is: 1 of 5, scattered. | Constraints and rules solve this better and cheaper than a model does. We'd talk you out of it. |
Recognise two or three of these rows?
Tell us which onesHow we work
01–03
We start with your business rather than the tech. You get an honest view of where AI saves you time or money, and where it does nothing, before anyone spends a dollar building.
A working slice ships early and goes in front of real users. You judge something that runs instead of a description of it.
We measure it against the case we set out, then hand it over with the documentation to run it. We succeed when you stop needing us.
Engagement scope · specimen
Nimbus · Melbourne
Want this document for your problem?
Request a scopeData path, in words: your files, records and systems stay where they already live. A retrieval layer running inside your own cloud account selects, per question, the few passages needed to answer it. Only the question and those passages cross the boundary to the model. Whole databases, credentials and identifiers the answer doesn't need never cross it. The answer returns into your app with its sources and a log of what was sent.
Your files, records, mailboxes and databases stay where they already live. Anything we index so it can be searched is built and stored inside your own account.
Everything you holdRetrieval, redaction and logging run in your own AWS, Azure or Cloudflare account. Per question, this layer picks the few passages that can answer it and strips the rest.
Only what this answer needsThe question, and those passages. Nothing else: no standing access to your systems, no bulk copy of your data, no credentials.
A slice, for one questionInto your app or the tools your team already uses, with its sources attached and a record of what was asked and what was sent. You or an auditor can check it afterwards.
The answer, its sources, and the recordIt depends on the job you point it at, which is why we start by telling you where it won't. It pays off on repetitive work: the questions your team answers all day, the documents someone retypes. We measure accuracy and cost. You find out whether it pays before you commit.
We scope each engagement with a fixed price up front. You know the cost before anyone builds. We publish the shape of that scope on this page. Starting small means you see results in weeks instead of committing a large budget on day one.
Yes, with the right controls. You decide what the model is allowed to see, and it only ever sees the passages needed to answer one question. Your files, credentials and databases stay in your own cloud account, and we do the security work an auditor will ask for.
No. You get a runbook, documentation walked through with the people who will own it, and the accounts, access and code in your names. No lock-in.
Nimbus is based in Melbourne and works with businesses across Australia. Your systems stay in your own cloud account. Where your team sits is not a constraint.
We design, build and secure AI systems that run in production, on cloud you control. One team handles the AI, the cloud it runs on and the security around it. Nothing falls between three vendors.
Too much AI consulting ends in a slide deck and an invoice, with a pilot nobody kept running. We build the thing and make sure it works. We come from running cloud and AI systems in production. We know what breaks, and we design around it.
Engagements stay small and fixed-scope on purpose. That keeps us honest and keeps you in control of the budget. The people who scope the work are the people who build it, so you never fund a junior team learning on your problem.
Nimbus at a glance
One short conversation, no pitch, tells you whether AI can help here and what it would take to find out. Send us a line about the problem and we'll reply within a business day.