Custom AI for the admin work templates and rules cannot handle.
I build custom AI workflows and AI agents that read enquiries, check documents, draft replies, summarise cases, prepare reports, and pull useful answers from scattered systems — using your real services, documents, and business rules. You see a working preview before deciding whether to pay.
I write the software myself. You own it. No subscriptions. No lock-in.
"Incredibly attentive, thorough and helpful through the whole process. Always very quick to reply."
— Emma Cichero, iLoans
"Excellent service throughout the whole process. I'm very happy with the outcome and would have no hesitation recommending Revamp Studios."
— Tim Henderson, Henderson Cars
Use AI where a person normally has to read, judge, or draft.
If the work is just moving a field from one app to another, you probably do not need AI. If someone has to read the message, understand the situation, check what is missing, write a useful response, or prepare a report — that is where custom AI starts to make sense.
Use plain automation when
The steps are clear and repetitive — moving data between apps, sending reminders, or updating a system on a schedule.
Use AI workflows or agents when
Someone would normally need to read what came in, work out what matters, and prepare the next step using your business context.
Often they work together
The AI handles the thinking part, then automation moves the result to the right system, person, or follow-up step.
Where your tools hit their limit
These are the situations that fall through the cracks — too variable for a rule, too tedious for a person.
"Every enquiry is different. No template fits."
Someone writes in explaining a specific situation. Your auto-responder sends "thanks, we'll be in touch." They feel ignored. The ones worth responding to personally stack up — and half don't get a proper reply until it's too late.
"Documents arrive in every format. Someone has to read each one."
PDFs, scanned forms, email attachments, photos of handwritten pages. Every one is slightly different. Someone opens each one, reads it, extracts what matters, checks what's missing. Every time, by hand.
"I want one clear picture of the business. The data is in five places."
Revenue in Xero, leads in the CRM, jobs in ServiceM8, bookings somewhere else. No single view without someone logging into each one, copying the numbers, and writing it up — usually you, usually on a Monday morning.
The numbers behind the problem
Well-documented, across thousands of businesses.
more likely to qualify a lead if you respond within 5 minutes versus 30 minutes
Oldroyd / InsideSales.com, Harvard Business Review
Australian business owners spend nearly a full working week every month on financial admin alone
Dext, 2025 — 500 Australian SMB leaders
of work activities in most businesses can be automated using today's AI technology
McKinsey Global Institute, June 2023
of Australian small businesses using AI report stronger revenue growth as a result
Salesforce SMB Trends Australia, 2024
What you can actually delegate to it
Think of the boring-but-useful work you'd hand to a sharp staff member: read this, summarise this, draft this, check this, chase this, and tell me what matters. Not templates. Not off-the-shelf tools. Software I write specifically for your business.
Read enquiries and draft real replies
When a message comes in, AI reads what was actually written, works out what they need, and drafts a reply that addresses their specific situation — in your voice, drawing on your services and pricing. Not a template. A real answer.
Process documents in any format
PDFs, scanned pages, email attachments — AI reads each one, extracts exactly what your process requires, checks against your specific criteria, and tells you what's complete and what's missing. No manual review.
Generate content that sounds like you
Quotes, progress reports, review responses, client summaries — AI generates them from your real data, in your voice, without you starting from a blank page every time. You review and send.
Pull a clear picture from scattered data
Revenue, jobs, leads, outstanding invoices — all in different systems, none of them talking. AI pulls from all of them, synthesises what matters, and delivers a plain-English summary on a schedule. One view. No logging in.
Check applications and intake packs
It can review what has been submitted, compare it to your requirements, and flag the gaps before it lands on your desk.
Prepare call and meeting briefings
Before a call, it can pull together the client history, recent activity, open items, and suggested next questions into one briefing note.
A customer-facing AI agent that knows your business specifically
Not a generic chatbot. AI trained on your exact services, pricing, availability, and how you talk — that handles customer questions, books appointments, and captures leads 24/7. It knows the difference between what you do and what you don't. Generic tools don't.
What this looks like in practice
Three situations where the gap between "tool handles it" and "person has to do it" closes.
Finance Broker
Document processing
Before
Client submits a home loan application with 12 documents attached. Broker opens each one, checks it against the requirements, notes what's missing, emails the client a list. 45 minutes per application, every application.
After
Client submits documents. AI reads every one, cross-references against the broker's specific checklist, and generates a summary: what's there, what's missing, what needs clarification. Broker reviews in two minutes and sends.
"No tool does this out of the box. Every broker's checklist is different."
Trade Business
Enquiry reading and response
Before
Eight to ten enquiries a day, each describing a different situation. Owner reads every one between jobs and types a personalised reply. The ones that come in after hours sit until morning. Two hours of the owner's day, every day.
After
AI reads each enquiry as it comes in, drafts a specific reply that addresses what they actually asked — referencing their situation, the relevant service, a rough idea of next steps. Owner reviews, approves, sends. Or it goes automatically for the routine ones.
"The auto-reply template was there before. This is different."
Consultant / Advisor
New client intake brief
Before
New client submits an intake form and uploads a few documents. Someone reads the form, opens each document, works out what they actually need versus what they said, spots what's missing, and prepares talking points for the first call. 45 minutes of concentration, usually the night before.
After
Submission comes in. AI reads the form and every document, produces a one-page brief: who this client is, what they've said they want, what the documents actually show, anything missing, and suggested opening questions tailored to their situation. Advisor reads it in ten minutes and walks into the call already knowing the client.
"I spend the whole first meeting figuring out what they actually need. The form tells me almost nothing."
How I keep it useful in real work
AI is only useful if your team can trust what it does. I give it a clear job, limit what it can access, shape its output so your systems can use it, and keep people in the loop where mistakes would matter.
Clear boundaries
The AI is built around a defined job, not left to wander through your business.
Tool limits
It only gets access to the actions and data it needs.
Human approval
Drafts, checks, and recommendations can stop for review before anything is sent or changed.
Test runs
Known examples can be run again after changes to catch worse answers, slower runs, or higher costs.
Same service. Two ways to run it.
Cloud-hosted
The best option for most businesses. Your AI runs in the cloud, I handle the setup around your business context and tools, and performance is usually stronger for most real-world tasks.
On-prem / in-office
For businesses with serious privacy, data-control, or compliance requirements that still want to use AI. It runs on dedicated hardware in your office, which you will need to purchase. I pass that hardware through at cost with no markup.
Important: the service itself is the same either way. The difference is where it runs. Choose on-prem because you need tighter control over sensitive data — not because it is better for general performance. In most cases, open-weights models running locally will not perform as well as the best cloud models for harder tasks.
Not a subscription. Not a template. Software.
Written from scratch
I write the software myself, built around exactly how your business works — not a generic tool configured for you.
You own the code
No subscription for the software itself. No monthly fee to keep it running. No limits imposed by what a platform allows.
See it before you pay
I build it, show you a working demo, and you decide. Same risk-free model as everything I build — $0 until you approve it.
Questions I hear all the time
Is this just a chatbot?
No. A generic chatbot answers general questions. What I build is set up around your own services, documents, and rules so it can do useful work for your team — not just talk.
How is this different from just using Zapier or Make?
Those tools connect systems with rules — if X happens, do Y. They're great for predictable, structured tasks. What I build adds AI on top: reading unstructured text, understanding context, generating responses. Things that can't be pre-programmed because every instance is different.
Do I need to change my existing software?
Usually not. The AI works alongside what you already have — reading emails from your inbox, pulling data from Xero or whatever you use, posting results back where they need to go. No ripping and replacing.
What if the AI makes a mistake?
I design around that with review points, clear rules, and sensible guardrails. The goal is to reduce workload without creating sloppy output.
How do you figure out what to build?
We start with a conversation about where your time actually goes. I ask questions, map out the workflow, and tell you honestly what AI can and can't help with. If it's something your existing tools can already do, I'll tell you that too.
How long does it take?
Depends on complexity. Most projects are live within 3–8 weeks from the first conversation. I build it, test it, and show you a working demo before anything goes live.
What if we have strict data security or compliance requirements?
That is exactly why the on-prem option exists. Most businesses should use cloud-hosted AI, but if you need tighter control, I can set it up on dedicated hardware in your environment. That hardware is passed through at cost with no markup, and it is important to understand that local open-weights models usually will not match the best cloud models for harder tasks.
What does it cost?
Projects start from $2,500, quoted in writing before any work starts. You own everything I build — there's no ongoing fee for the software itself. Any third-party AI costs (like API usage) are passed at cost with no markup.
Do I own what you build?
Yes. Same as all my work — you own the result and there is no lock-in.
Tell me what's eating your time.
Book a free 30-minute call. Walk me through where your day actually goes. I'll tell you honestly what AI can fix, what your existing tools already cover, and what a custom build would cost.
No pitch. No jargon. A straight conversation about whether this makes sense for your situation.