Practical AI development and business automation
Most businesses do not need an AI strategy. They need one or two specific things done automatically that are currently done by a person, and an honest answer about which of their ideas are worth building.
Does this sound familiar?
You are probably here because of one of these
Your team spends hours on work that is essentially reading, summarising, classifying or extracting information from documents.
You want to add AI features to an existing product but do not know where they would genuinely help.
You have tried an AI tool, seen it work in a demo, and found it unreliable on your actual data.
You are being quoted large sums for AI capability and want a second opinion from someone who has actually shipped it.
You are concerned about what happens to your data when it goes to an AI provider — reasonably so.
What this covers
The work itself
Not a capability list — these are the specific things an engagement in this area actually involves.
AI features in existing applications
Adding genuinely useful AI capability to software you already run — search that understands intent, document processing, summarisation, classification, drafting — integrated into the existing system rather than bolted alongside it.
LLM integration
Working with multiple model providers behind a clean internal interface, so you are not locked into one vendor and can change as the models change. This is the architecture running PotatoAIHub in production today.
Workflow automation
Automating multi-step business processes, with AI used for the steps that genuinely need judgement and ordinary code for the steps that do not — which is usually most of them, and considerably cheaper.
AI product development
Building products where AI is the core rather than a feature: asynchronous generation, queue processing, cost control per request, and handling providers that fail.
Honest assessment
Sometimes the answer is that AI is an expensive way to solve a problem that had a simpler solution. You will be told that, because a project that should not have been built helps neither of us.
Typically involves
- LLM APIs
- Laravel
- PHP
- Queue processing
- AWS
- Vector search
- REST
How the work runs
The approach
Consistent across engagements, because the order these things happen in is usually what determines whether a project goes well.
- 01
Start from the task, not the technology
The useful question is which specific, repeated, expensive task you want handled — not where AI could be applied. AI projects that begin from the technology tend to produce demonstrations rather than value.
- 02
Test against your real data
AI features behave very differently on curated examples than on the messy data a business actually holds. Viability gets tested early against the real thing, before significant money is committed.
- 03
Design for cost and failure from the start
Every request costs money and any provider can fail. Both are architectural concerns, not operational surprises — as running a live AI platform makes unavoidably clear.
- 04
Keep a human where judgement matters
Automating a decision is different from automating the work of preparing it. Which of the two you want should be a deliberate choice, not a side effect of how the system was built.
Evidence
Where this has been done
Real projects involving this work, with the reasoning and the results.
Questions
What people usually ask
- What actually qualifies you on AI?
- PotatoAIHub — a live multi-model AI platform at potatoaihub.com, serving chat, image and video generation in production. It was built and is operated end to end, which means the integration patterns, async processing and cost control offered to clients are already running in something real rather than described from documentation.
- Will our data be sent to AI providers?
- That depends on the architecture, and it is a decision to make deliberately rather than discover afterwards. What data leaves your systems, which providers see it, what they retain, and what can be kept local are all design questions worth settling before anything is built.
- Is AI right for our problem?
- Sometimes not. A rules engine, a better database query or a fixed process is often a cheaper and more reliable answer. Working out which situation you are in is a legitimate first engagement and does not require committing to a build.
Have a software problem, project or idea?
Tell me what you are trying to achieve and where it is currently going wrong. You will get an honest read on it from someone who has built this kind of thing before — including if the answer is that you do not need what you were about to buy.
Prefer not to call? Send me a message
Working with businesses across Australia — Melbourne, Sydney, Brisbane, Adelaide, Perth, Canberra and regional Australia.