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AI assessment and deployment for Australian businesses
A free 30-minute assessment to find where AI would save your team time, then fixed-price builds and an ongoing retainer to put it to work.
Free AI assessment
Find your first AI win in about a minute
Answer a few questions about where your team loses time or money. A senior developer reads them before your free 30-minute call.
- No obligation, and no sales sequence afterwards.
- Plain-English summary of what is worth doing and what is not.
- Senior Australian developers, not a call centre.
Included
What’s included
Free 30-minute AI assessment
A call about your bottlenecks, the tools you use and the hours involved, followed by a short written summary of what is worth doing and what is not.
Use-case ranking
The tasks where AI can work reliably, ranked by time saved against cost to build and run, so the first build is the one that pays back fastest.
Fixed-price first build
The first feature built into your existing software, so it shows up in the workflow rather than as another tab to check.
Retrieval over your own content
Question-and-answer and search across your own documents and records, with answers that cite where they came from.
Guardrails and review
Checks on the output and a way for staff to correct it, with logging so you can see what the system did and why.
Ongoing retainer
Monitoring, tuning and new workflows month by month, with usage cost and model choice managed as better or cheaper options appear.
Process
From first call to running system
Each step stands on its own. You can stop after any of them and keep what you paid for.
1. Free assessment
A 30-minute call about where time goes in your business. You get a short written summary of the opportunities and our view on which one to do first.
2. Discovery
For anything bigger than a small feature, a fixed-fee Discovery produces the written scope and a fixed quote. It is credited against the build if you build with us.
3. First build
One narrow feature, built into your existing systems at a fixed price, with a person kept in the loop on anything that carries risk.
4. Monthly retainer
Optional. We keep the feature running, tune it, and add the next workflow when the first has proven its value.
The assessment
What happens in the free 30 minutes
It is a working conversation with a senior developer, not a sales pitch. We would rather tell you AI is the wrong tool than sell you a build that does not pay back.
What we ask about
The task that eats the most hours, how often it happens, which tools it touches, who does it today and what a mistake costs.
What you leave with
A written summary of the best opportunities, a rough cost to build and run the first one, and a plain recommendation on whether to proceed.
Who it suits
Small and mid-sized Australian businesses with a repeatable process that takes staff time: admin, enquiries, quoting, document handling, reporting.
Who it does not suit
Businesses after an AI strategy deck, a chatbot for its own sake, or a general training session on ChatGPT.
Fit
Where AI earns its place
AI pays off on specific tasks with clear inputs and outputs you can check. It is a poor choice when a plain rule would do the job, or when a wrong answer costs more than a slow one.
Good fits
Sorting incoming email, drafting a first version of a document, pulling data out of PDFs and forms, checking submissions for errors, and answering questions across your own documents.
Poor fits
Decisions where nobody can check the output, tasks a simple rule already solves, and work where being wrong is expensive. In those cases we will say AI is not the answer.
Comparison
Choosing the right tool for the task
| Rule or script | Smaller model | Large language model | |
|---|---|---|---|
| Best for | Fixed logic with clear conditions | Classifying and extracting from text | Drafting, summarising and answering questions |
| Running cost | Negligible | Low | Highest per use |
| Example | Send invoices over a set amount to approval | Tag incoming emails by topic | Draft a reply from a customer’s history |
| Checking the output | Predictable | Spot checks and thresholds | A person or a rule reviews it first |
Examples
What we build
Inbox triage
Incoming email sorted, summarised and routed to the right person.
Document drafting
A first version of a report, quote or letter for a person to review.
Search and answers over your documents
Questions answered from your own records, with a citation to where each answer came from.
Data extraction
Fields pulled out of PDFs, forms and invoices into your system.
Form and error checking
Submissions checked before a person spends time on them.
Assistants wired into your systems
An AI feature inside the tools your team already uses, not another tab.
Retainer
What the monthly retainer covers
A retainer makes sense once a first build is running and there is a queue of further workflows worth doing. It is quoted individually after the assessment, because the right size depends on how much is running and how much you want added each month. The scope is agreed in writing before it starts.
New workflows
The next bottleneck from the assessment, built and added once the previous one has proven its value.
Monitoring and tuning
We watch how often staff override the output and fix the prompts, data and rules behind it.
Cost and model management
Usage cost tracked against the time saved, with the model swapped when a better or cheaper one fits.
A monthly summary
What ran, what it cost, what staff corrected and what we recommend next, in plain English.
Safety
How we keep it trustworthy
A person in the loop
Anything that carries risk is reviewed by a person or checked by a rule before it takes effect.
Every call logged
You can see what the system did and why, so mistakes can be traced and fixed.
Your data stays yours
Business API tiers where your content is not retained for training, or models on infrastructure you control for sensitive material.
Australian privacy
Built with the Australian Privacy Act in mind, with on-shore hosting in most cases.
Cost
What AI deployment costs in Australia
The assessment is free. Builds land roughly between $5k and $100k+, depending on size, complexity and deliverables. A first, narrow feature is a small build, and you get a fixed price before we start. Running cost depends on volume and the model, and we estimate it up front.
| Project size | Typical cost | What it usually covers |
|---|---|---|
| Small | $5k–$10k | One narrow feature in one workflow, such as triage or extraction, with review |
| Medium | $10k–$30k | Several features, or search across your own documents, wired into your systems |
| Large | $30k–$100k+ | AI woven through a platform, with custom retrieval, evaluation and monitoring |
Larger builds start with a fixed-price Discovery, which is separate from the build cost. Retainers are quoted individually after the assessment. We usually add the feature to software you already run, see existing software. More on pricing in the FAQ.
If this sounds like your project, Discovery is the next step.Start with a fixed-price Discovery — you get the scope, the plan and the number before you commit.
See how Discovery works →Approach
How we approach it
Understand
We start with the work, not the technology. We learn where time goes and pick a task where the input is clear and the output can be checked, and where being slow today carries a real cost.
Simplify
We try the plainest thing that could work first. Often a small, well-scoped feature beats an ambitious one, and sometimes the answer is not AI at all.
Build
Senior developers build the feature into your existing system in short cycles, with a person kept in the loop on anything that carries risk.
Improve
After launch we watch how often staff override the output and what the feature costs to run, then tune the prompts and the data behind them and change the model if a better fit appears.
Stack
Tech we use
An honest starting point, not a fixed menu. We pick per project and tell you why.
- OpenAI
- Anthropic
- Google Gemini
- Open-weight models where they fit
- Python
- TypeScript
- pgvector
- RAG
- PostgreSQL
- Your existing database and stack
- AWS
FAQ
Common questions
- Is the AI assessment really free?
- Yes. It is a 30-minute call with a senior developer and a short written summary afterwards, with no obligation to build anything. We do it because the best way to show whether AI helps your business is to look at the actual work.
- What happens after the assessment?
- If there is a task worth automating, we give you a rough cost to build and run it. A small feature can go straight to a fixed-price build. Larger work starts with a fixed-fee Discovery, which is credited against the build if you build with us. You can stop at any point.
- What does the monthly retainer include?
- Keeping what we have built running and tuned, tracking usage cost against the time saved, adding new workflows, and a plain-English monthly summary. It is quoted individually after the assessment, and the scope is agreed in writing.
- Do we need a retainer?
- No. Many businesses build one feature and run it themselves, and the code is yours. A retainer suits businesses that want a queue of workflows built and the running ones looked after.
- What is AI integration?
- AI integration means building an AI capability, such as drafting, search or data extraction, into the software a business already uses, so it shows up in the normal workflow. It usually involves connecting to a model through an API and adding checks on the output.
- How much does AI development cost in Australia?
- Projects land roughly between $5k and $100k+, depending on size, complexity and deliverables. A first, narrow feature is about $5k–$10k, and wider integrations $10k–$30k or more, at a fixed price. Running cost is estimated up front.
- Which AI models do you use?
- OpenAI, Anthropic and Google Gemini models, and open-weight models where they fit. We choose per task for quality, cost and data sensitivity, and we can swap the model later as better or cheaper options appear.
- Is our data used to train an outside model?
- Not on the setups we build. We use business API tiers and configurations where your content is not retained for training, and for sensitive material we can run smaller models on infrastructure you control.
- What if the AI gets it wrong?
- We assume it sometimes will. That is why we scope tasks where the output is checked, by a person or by a rule, before it does anything that matters. We also log every call so mistakes can be traced.
- Do we need a large budget to start?
- No. The assessment is free, a first, narrow feature is a small build, and the running cost for one task is usually minor. Starting small is also the right way to find out whether AI helps before committing further.
Bespoke SaaS
Your own platform, built to your process — not another off-the-shelf tool you have to bend around.
Learn more →Existing Software
Inherited something half-built or stalled? We take it over, tidy it up, and move it forward.
Learn more →Last updated