AI Agents Built by Operators,
Not Just Technologists.

We go department by department and look at every process. Each one gets documented and scored: can an AI agent handle this on its own? Does it need a person with AI backing them up? Or is it better left alone? If something doesn't need AI, we'll say so. You end up with a clear picture of where automation actually makes a difference and where it doesn't.

Not at all. We work with schools, universities, government departments, NGOs, healthcare providers, professional services firms, anyone really. If your organisation has people doing repetitive work, there are probably processes we can automate with AI agents.

Then we'll tell you that. Not every organisation needs AI agents, and we're not going to pretend otherwise. The process discovery is valuable on its own though. You get every process documented with proper SOPs, even if the conclusion is "you're doing fine without AI." We'd rather lose a sale than sell you something that won't help.

Either way works. We can keep managing everything for you: agents running day-to-day, our team handling exceptions, weekly reporting, the lot. Or we build the agents, document how they work, train your people, and step back. Most clients start with us running things and gradually take over as they get comfortable.

OpenClaw is an open-source AI agent platform. We install it, configure it, and hook it up to your GitHub on your own servers, whether that's on-prem or in the cloud. You get autonomous AI agents running without paying per-call API fees, and your data stays on your infrastructure.

Yep. We deploy self-hosted n8n on your infrastructure using Docker. Unlimited workflows, no per-execution charges. We design the workflows, connect your APIs, and show your team how to use it. Works out much cheaper than Zapier or Make, especially at scale.

Services like OpenAI and Anthropic charge per token, so costs go up the more you use them. Self-hosted models like Llama and Mistral run on your own hardware for a fixed monthly cost, no matter how much you use them. We handle the GPU setup, model deployment, and tuning so it just works.

Think of it as software that does a job on its own, start to finish. Unlike a chatbot that just answers questions, an agent takes action across your systems. A few real examples:

Accounts Payable: An invoice lands in your inbox. The agent reads it, pulls out the line items, validates the ABN and GST, matches it to the purchase order, and posts the bill to your accounting system. If something doesn't match, it flags it for a human. If it's under the approval threshold, it schedules the payment automatically.

Accounts Receivable: The agent tracks every outstanding invoice. It sends payment reminders at 7, 14, 30, and 60 days. If someone still hasn't paid at 90 days, it drafts a formal notice and escalates to your finance manager. It updates the aging report daily without anyone asking.

Tax and Compliance: Come BAS time, the agent pulls all the sales and purchase data, validates the GST calculations, compiles the return, and generates a lodgement-ready report. Your accountant reviews and submits. What used to take days takes hours.

Recruitment: A hiring manager submits a role. The agent writes the job description based on the requirements, posts it to your job boards, then starts screening as applications come in. It parses every resume, scores candidates against the job criteria, produces a ranked shortlist with match percentages, and flags any gaps. For your top candidates, it can run background verification checks and schedule interviews. Your recruiter goes from spending 30 minutes per resume to reviewing a ready-made shortlist over coffee.

That's the difference between a chatbot and an agent. One answers questions. The other does the work.

Depends on your business, but the ones that pay for themselves fastest are: accounts payable (reading invoices, matching POs, posting to your accounting system), call handling (picking up calls, understanding what the caller needs, routing to the right person), customer care (triaging emails, reading the sentiment, responding to common queries, escalating the tricky ones), HR screening (sorting through hundreds of resumes and shortlisting candidates), employee support (answering staff questions about leave, policies, payslips without bothering HR), email management (reading incoming emails, figuring out who should handle them, forwarding accordingly), and procurement (comparing supplier quotes, generating purchase orders). We go through your operations first and figure out which ones make the most sense for your situation before we build anything.

Traditional automation follows fixed rules: if X happens, do Y. AI agents are different. They read context, handle messy data like emails and PDFs, deal with exceptions, and get better over time. A script breaks when the invoice format changes. An AI agent figures out the new format and keeps going. We deploy these agents across finance, HR, operations, wherever they're needed.

It varies a lot depending on what you need. Our process discovery starts at A$8,000 as a one-off. If you want us to build and manage agents on an ongoing basis, that's A$20,000 a month. Enterprise engagements are custom. Our hourly rate is A$200 + GST. Compared to hiring even one extra admin at $65-80K a year, the agents usually pay for themselves within the first couple of months. We also do self-hosted setups so you're not paying per-token API costs that blow out as you scale.

An AI agent costs a fraction of a full-time employee and works around the clock. It doesn't take leave, doesn't have bad days, and handles repetitive tasks consistently. But it's not a replacement for people. Your team still handles the exceptions, the relationship stuff, the judgement calls. Think of it this way: instead of hiring two more admin staff to handle growing volume, you deploy agents that handle the volume and let your existing team focus on the work that actually needs a human brain.

We take this seriously. All our deployments run on Australian infrastructure, so your data doesn't leave the country. We can self-host everything on your own servers if you need full control. We follow the Australian Privacy Principles and work within the Privacy Act. For clients in regulated industries, we can deploy on-prem with no external API calls at all. Your data is yours. We don't train models on it, we don't share it, and we don't send it offshore.

Studies say around 85% of AI projects don't make it to production. The main reason is people jump straight to building without understanding the problem. They automate the wrong processes, pick the wrong tools, or build a demo that looks great but falls apart with real data. We avoid this by starting with discovery. We map your actual operations, identify what's genuinely worth automating, and only build agents for those. Then we manage them in production, not just hand over a prototype and wish you luck.

No. That's the whole point of our managed service. We handle the technical side: building the agents, connecting them to your systems, monitoring them, fixing things when they break. Your team just uses them like they'd use any other tool. If you eventually want to manage things in-house, we train your team and hand everything over with full documentation.

That's exactly what the discovery phase is for. As a general rule, start with processes that happen frequently (daily or weekly), follow a predictable pattern, involve lots of data entry or copy-pasting between systems, and take up more of your team's time than they should. Invoice processing, email triage, resume screening, and routine customer queries are usually the best starting points. But every organisation is different, which is why we look at yours specifically before recommending anything.

Most AI companies are tech-first. They know how to build models and write code, but they've never actually run a payroll, processed a purchase order, or managed accounts receivable. We have. Over 30 years of it. We've managed, optimised, and automated business processes long before AI was in the picture. So when we build an AI agent for your AP department, it's not a developer guessing how invoices work. It's someone who's processed thousands of them knowing exactly what the agent needs to do, what can go wrong, and where a human still needs to be involved. That subject matter expertise is what turns an AI demo into something that actually runs in production.

Good question. Ask them: do you do process discovery before building anything? Can you show agents running in production, not just demos? Where does my data go? What happens if I want to leave? Do you manage the agents after deployment or just build and disappear? What's the pricing model and are there hidden costs? If they can't answer these clearly, keep looking. We're happy to answer all of them.

No. AI gives your people the ability to do more in less time. The things they couldn't get to before, they can now. Your finance person isn't going anywhere. They just won't spend three days matching invoices anymore. Work-life balance was always hard when people were buried in repetitive tasks. With AI handling that stuff, it's actually possible. The whole point is to empower your team, not get rid of them.

The biggest risk is deploying them without proper oversight. AI agents can make mistakes, especially with edge cases they haven't seen before. That's why we always build in human-in-the-loop checkpoints for anything that matters, like payments, legal documents, or sensitive communications. Other risks include data privacy (solved by self-hosting), over-reliance on AI for things that need human judgement (solved by our honest assessment process), and vendor lock-in (solved by building on open-source tools wherever possible).

Melbourne. We work with clients across Australia and a few overseas. The agents run around the clock, and our team works AEST hours. All data stays on Australian infrastructure.