Kontrol AI designs and deploys custom AI workflows for businesses with operational complexity — connecting your systems, cutting the manual admin, and giving your team back kontrol.
Chris Olanda is the founder of Kontrol AI, a Gold Coast AI workflow automation consultancy operating as CO Advisory Pty Ltd (ABN 85 693 421 788). He builds automation for Australian trade contractors and professional services firms — connecting existing platforms such as email, SimPRO, Xero, ServiceM8 and CRMs so that job intake, quoting, dispatch and invoicing run without manual re-keying.
Before founding Kontrol AI in 2026, Chris worked in risk, operations and digital transformation at Macquarie Bank, redesigning the controls and processes behind financial operations at scale. He holds a Master of Business. That background is why Kontrol AI builds for efficiency, impact and safety: every automation carries a full audit trail, is robust, and anything ambiguous is routed to a human instead of guessed at.
His production systems have handled 1,002 automation runs across live client pipelines — processing 728 jobs at a 99.6% success rate and removing 13.3 hours of administrative work per week.
Kontrol AI is based on the Gold Coast, serves Brisbane and South East Queensland, and works with clients nationally.
AI workflow automation is software that runs an admin process end to end — reading an email, extracting the details, creating the job in your system — making the judgment calls a person would otherwise make along the way.
Traditional automation follows fixed rules: if this exact thing happens, do that. The AI layer adds interpretation, so the input no longer has to arrive in a predictable shape. A workflow can read an unstructured request, work out that it is a job, pull the site address, the fault and the urgency out of the text, and create the record in SimPRO or ServiceM8 without anyone re-keying it. Kontrol AI (CO Advisory Pty Ltd), a Gold Coast consultancy, builds these on n8n; its live client pipelines have processed 728 jobs and removed 13.3 hours of admin a week as at 3 August 2026.
Regular automation moves data between apps when conditions match exactly. AI automation handles input that varies — free-text emails, PDFs, photos, call transcripts — by interpreting it first, then handing off to the same reliable rules.
Most businesses already run some rule-based automation: a Zapier connection, an invoice template in Xero, an alert in their job management system. Those break the moment the input isn't in the expected shape, which is why inbound email is still usually handled by hand. The pattern that works is a hybrid — the AI layer reads and structures the messy input, then deterministic steps do the actual writing into SimPRO, Xero or a CRM, so every action is logged and repeatable. Across 1,002 automation runs, Kontrol AI's pipelines have recorded a 99.6% success rate on that model.
RPA mimics a person clicking through a screen, so it breaks when the screen changes. Workflow automation connects systems through their APIs instead, and an AI layer on top reads the input a fixed rule can't interpret.
RPA — robotic process automation — was built for older software with no API, and it drives the user interface the way a person would. It works, but it is brittle and expensive to maintain. Where a business runs modern cloud tools such as Xero, SimPRO, ServiceM8 or Microsoft 365, an API-based workflow is more reliable and far cheaper to run, because it talks to the system directly instead of pretending to be a user. For most Australian SMEs the practical answer is API-first, with RPA reserved for a legacy system that genuinely has no other way in.
The realistic wins are high-volume admin with a describable process: email and job intake, quoting, scheduling and dispatch, invoicing, data entry between systems, and report generation. Work needing negotiation, physical presence or a real relationship does not.
A useful test is whether the task is repetitive, whether someone could write the process down, and whether a right answer would be checkable. Job intake, invoice preparation and compliance reporting all pass it. A pricing negotiation with a long-standing client does not. Australia's National AI Centre put SME adoption at 43% across December 2025 to February 2026, with fewer than 30% of construction businesses using AI against more than half in the services sectors — so in trades in particular, most of this work is still being done by hand. Which task to automate first is a different question, usually answered by looking at where the hours go.
You can for drafting, and it's worth doing. What ChatGPT can't do is run without you: an automation is connected to your inbox and your job system, and it executes at 2am when nobody is at a desk.
The gap is integration, reliability and maintenance rather than intelligence. The work is mapping the real process, deciding which steps are safe to automate, connecting email, SimPRO, Xero, ServiceM8 or a CRM with authenticated and permissioned access, handling the edge cases that break naive workflows, and monitoring the thing once it is live. Copying output out of a chat window still leaves a person moving information between systems — and that is the step that was costing the hours in the first place.
An automation handles the repetitive, describable part of an admin role — intake, data entry, invoicing — at any hour and at consistent quality. It cannot exercise judgment, hold a client relationship, or notice that something feels wrong.
The realistic comparison is capacity, not replacement. Two live Kontrol AI pipelines together remove 13.3 hours of admin a week — roughly two days of someone's time — by taking the typing, not the thinking. What is left is the work that actually needs a person: the unusual job, the awkward client conversation, the call about a variation. The honest limitation is that automation covers only the part of a role you can describe as a process. Anything requiring discretion, escalation or a phone call stays with a human.
It can be, if it is designed for it. The controls that matter are where the data is processed, whether it is used to train third-party models, who can access it, and whether every action is logged.
Australian businesses handling personal information sit under the Privacy Act 1988 and the Australian Privacy Principles, administered by the OAIC — an automation inherits those obligations rather than changing them. From 10 December 2026, businesses using automated decision making that significantly affects individuals must also disclose it in their privacy policy. In practice that means business API tiers where inputs are not used for model training, credentials held in a secrets store rather than in the workflow, each integration limited to the permissions it needs, and a full audit trail. Kontrol AI builds every workflow with that trail, and routes anything ambiguous to a human.
No. Most AI automation connects to the systems you already run — SimPRO, ServiceM8, Xero, MYOB, Microsoft 365, your CRM — through their existing APIs. Replacing a job management system is rarely the right first move.
Automation sits between systems rather than inside them, so a typical build connects your email, job management and accounting tools and moves structured data between them. That matters commercially: a platform migration is a months-long project carrying retraining and data risk, while a workflow connecting what you already own is a far smaller piece of work. Kontrol AI builds on n8n as the orchestration layer for exactly this reason — it connects to standard business APIs without asking your team to learn different software.
Four things move the price: how many jobs or invoices run through the process each month, how many systems must be connected, how variable the incoming information is, and how many exception cases need a rule.
Published ranges in this market vary so widely that quoting one would be misleading — a single connection between two cloud systems and a multi-step pipeline handling hundreds of jobs a month are not the same product. What is consistent is the structure: a one-off build cost, then an ongoing running cost for hosting and model usage that is typically a small fraction of it. Kontrol AI (CO Advisory Pty Ltd), Gold Coast, scopes and prices each build after the free 60-minute workflow review, so the number comes from your actual process rather than an average.
Nothing. The Kontrol AI workflow review is a free 60-minute session that maps how the work currently moves, identifies where the admin hours are going, and comes back with the automation opportunities worth building. No obligation.
The review exists because scoping is the part that determines whether an automation is worth building at all. It covers the process as it actually runs, the systems involved, the volume going through it, and which steps could safely be handled without a person. What comes out is a short list of candidates and a view on which one pays back first. Nothing is charged before a scope is agreed, and there is no requirement to proceed to a build afterwards.
Mostly the time. The platforms an automation runs on cost tens of dollars a month; the work is mapping the process, connecting the systems, handling the exceptions and testing it against real data.
It is worth separating the two, because they behave differently. Software is a recurring subscription you would pay whoever built the workflow — n8n, the model provider, the server. Build effort is a one-off: the process mapping, the integration work against SimPRO, ServiceM8, Xero or a CRM, the decisions about what happens when an input is ambiguous, and the testing that stops a bad automated action reaching a customer. The second is where the cost sits, and it is also where the value sits — a workflow built on a process nobody mapped properly is the usual reason automation projects fail.
Three things recur: the automation platform the workflows run on, the AI model usage they consume per run, and any support arrangement you choose to keep. Support is optional and agreed at handover, not assumed.
Model usage scales with volume, because each run sends a small amount of text to a model and pays per token — a workflow processing a few hundred jobs a month sits at the low end of that. The platform cost depends on whether n8n is self-hosted or run on n8n Cloud. Support is where the variation is: some clients take a monthly arrangement, others take the documentation at handover and call when something changes. Kontrol AI's builds are documented and portable, so ongoing support is a choice rather than a dependency.
The self-hosted Community Edition is free and unlimited; a small cloud server to run it costs under $10 a month. n8n Cloud starts around US$24 a month for 2,500 workflow executions, with unlimited workflows and users.
n8n prices on execution volume rather than on workflows or seats, so the number of automations and the number of people using them do not change the bill. As at 3 August 2026 the published Cloud tiers run from roughly US$24 a month for 2,500 executions to US$60 for 10,000, with larger business tiers above that; n8n bills in US dollars and euros, not Australian dollars, so check the current page before budgeting. Self-hosting removes the licence cost entirely and leaves only the server — which is why Kontrol AI builds on n8n rather than on a platform that charges per task.
Because a subscription buys the connectors, not the process. Zapier moves data between apps when the trigger matches exactly; a custom build interprets messy input, handles the exceptions and writes into your job and accounting systems reliably.
The comparison is between a tool and a piece of work. Off-the-shelf automation is genuinely good for simple, predictable triggers, and where that is the whole problem it is the right answer — a consultant who tells you otherwise is selling. The cost appears when the input is a free-text email, a photo of a job sheet or a PDF, when a step has to be reversible, or when a field in SimPRO has to be right or payroll goes out wrong. Per-task subscription pricing also inverts at volume: the more the automation runs, the more it costs, where a self-hosted build does not.
Start with one workflow, not a program. Take the free review, pick the single process eating the most hours, build that one, and let it run for a month before committing to anything else.
The expensive mistake is scoping a whole transformation before anything is proven. One workflow is cheap enough to be a real test, and it produces the thing a spreadsheet cannot: evidence from your own data about whether the hours actually came back. It also surfaces the awkward parts of the process early, when changing the design is still cheap. Kontrol AI's own client pipelines were built this way — job intake first, then invoicing six weeks later, together removing 13.3 hours of admin a week as at 3 August 2026.
The ones that catch people out are hosting and model usage after handover, paid connectors for systems the platform does not support natively, changes to scope mid-build, and whether support is included or billed separately.
Two more are worth asking any automation consultant, because they are where the real money hides. The first is who holds the platform account: if the workflows live inside a vendor's own environment, leaving means rebuilding. The second is what happens when your job management system changes its API — someone has to fix it, and whether that is chargeable should be settled at handover rather than discovered later. Kontrol AI's builds are documented and portable, support is agreed at handover rather than assumed, and a scope change is re-quoted before the work is done rather than after.
Book your free 60-minute workflow review. We'll map your current workflow, identify the biggest automation wins, and give you a clear picture of what's possible — no obligation, and you'll be talking with the founder who builds the work.
Or email us directly: ai@kontrolai.com.au