How long does an automation project take?

A single, well-scoped process automation usually takes a few weeks from process mapping to going live. Projects involving multiple systems or custom AI-model work take longer. We give you a realistic timeline once we understand the scope.

Do we need technical staff on our side?

No. We work directly with whoever does the process today, technical or not. You don't need an in-house developer to work with us, though we're happy to work alongside one if you have one.

What happens to our data?

Your data stays within the systems and cloud providers we agree on together, and any AI-model data handling is scoped explicitly before it's used. Nothing gets sent anywhere without that being part of the agreed design.

Will automation replace staff?

Most of the projects we build free people from repetitive manual work rather than replacing roles outright. The aim is usually to remove the parts of a job nobody wanted to do, not the job itself.

Can this integrate with the software we already use?

In most cases, yes, through an API where one exists, or through RPA where it doesn't. We'll tell you upfront if a system genuinely can't be integrated.

What's the difference between automation and AI?

Automation follows fixed rules and is faster and cheaper to build. AI is used specifically where a judgment call is needed: reading unstructured text, handling variation a fixed rule can't. Most projects are mostly automation, with AI used only where it's actually needed.

Do you build custom AI models, or use existing platforms?

Almost always existing platforms (OpenAI, Anthropic, Azure OpenAI) configured for your specific use case. Training a model from scratch is rarely the right answer for a business automation project, and we'll say so if it isn't.

How do we know if a process is worth automating?

Generally: is it repetitive, high-volume, and rule-based (or close to it)? If yes, it's usually worth automating. If a process changes every month or needs genuine judgment every time, it usually isn't, and we'll tell you that honestly rather than build something that doesn't pay for itself.

What happens after the project goes live?

You get documentation covering what was built and why, and an agreed support arrangement. Automation only one person understands is a risk, so handover is part of every project, not an afterthought.

Is our data used to train AI models?

Not by default. We configure AI platform settings explicitly to opt out of model training where that option exists, and we confirm this as part of project setup rather than leaving it as an assumption.

AI & Automation

Who This Is For

You're repeating the same manual task every week: data entry, invoice processing, customer replies, report building, and it's taking hours that could go elsewhere.
You have systems that don't talk to each other: your CRM, accounting software and inventory system all hold pieces of the same information, and someone is retyping it between them.
You're curious about AI but want a straight answer (not a sales pitch) about what it would actually do for your business.

This probably isn't the right fit if your process is still changing month to month. Automation works best once a process is stable enough to be worth encoding. If that's not you yet, we'll usually say so rather than build something that doesn't pay for itself.

The Problem With Most "AI Automation" Projects

Most automation projects fail for a boring reason: nobody automates the actual bottleneck. A shiny AI chatbot gets built while the real time-sink, someone manually re-keying data between two systems every morning, stays exactly as manual as it was.

We start from the opposite direction. Before any automation or AI gets discussed, we map the process as it's actually done today, with the people who do it. That's usually where the real time-saving hides, and it's often solved with straightforward automation, not AI at all. AI gets used specifically where a genuine judgment call is needed at scale, not as decoration.

What's Included

Document & Invoice Processing

Extracting data from invoices, receipts, forms and PDFs, pushed straight into your accounting or ERP system without manual re-entry.

Customer Support & Chat Automation

First-response handling, ticket triage and FAQ resolution, with a clear handoff to a human for anything that needs one.

Workflow & Data Automation

Connecting the systems you already use: CRM, accounting, inventory, spreadsheets, so information moves between them without someone copying and pasting it.

Reporting & Business Intelligence

Turning scattered data into a report that updates itself, instead of someone rebuilding the same spreadsheet every Monday morning.

Custom AI Assistants & Copilots

Internal tools built on large language models (OpenAI, Anthropic, or Azure OpenAI, depending on your existing stack) for summarising documents, drafting responses, or answering questions against your own internal knowledge base.

Systems Integration & RPA

Where a proper API integration isn't available, robotic process automation (RPA) can operate existing software the way a person would, without needing the underlying system to change.

How We Approach an Automation Project

1. Process Mapping

We sit with the people doing the work today and map every step as it actually happens, not as the org chart says it should. This is where most of the real savings get found.

2. Automation vs. AI Assessment

For each step, we decide honestly whether it needs full automation, AI, or should simply stay a human decision. Not every step benefits from either.

3. Build & Integrate

We build against your existing systems rather than asking you to replace them: integrations where APIs exist, RPA where they don't.

4. Test With Real Data

Before anything goes live, it runs in parallel against real historical data so you can see exactly what it would have done, and catch edge cases before they matter.

5. Handover & Support

You get documentation for what was built and why, plus a clear support arrangement. Automation only one developer understands is a liability, not an asset.

Technology We Work With

Chosen based on what fits your existing systems and budget, not a fixed stack we push regardless of fit.

AI & Language Models

OpenAI, Anthropic (Claude), and Azure OpenAI Service, selected based on your data residency and compliance needs.

Automation & Integration

n8n, Zapier and Make for lighter integrations; custom-built middleware where an off-the-shelf tool can't handle the logic required.

Cloud & Hosting

AWS and Microsoft Azure, matched to whatever your existing systems already run on.

Why Work With Us

We're a Sydney-based team: the people mapping your process and the people building the automation are the same people you're talking to, with no handoff to an offshore team partway through the project.

What Automation Projects Typically Cost

Cost depends mainly on three things: how many processes are being automated, how many systems need to be integrated, and whether custom AI-model work is required or an off-the-shelf platform is enough.

A single, well-defined process automation is a smaller, faster engagement than a multi-system integration with a custom AI assistant layered on top. We scope every project individually rather than quoting a flat rate, because "AI automation" genuinely covers a wide range of actual work.

The most reliable way to get an accurate number is a short discovery call. We'll tell you honestly if a process isn't worth automating yet, even if that means a smaller (or no) project.

AI & Automation Sydney