AI that helps your numbers, not just your slide deck
We build AI on your real data and real workflows — so it ships and stays useful. We also say when a simple repeatable tool is better than AI.
Common signs you need help
- Your team keeps asking "what should we do with AI?" but nothing ships
- You tried ChatGPT on the side and it doesn't connect to your real tools
- Vendors sell AI features you don't need while the real problem stays stuck
Who this is for
This is for owners who have watched a team trial ChatGPT for three months without anything reaching a customer. Usually there is real enthusiasm, a few clever prompts saved in a document, and no connection between any of it and the CRM, the job data, or the inbox where work actually arrives. If you can name a task someone does daily that involves reading, summarising, classifying, or drafting, there is probably something here worth building.
Where this applies
Many Australian businesses trial ChatGPT in isolation while customer data and ops tools stay disconnected — we build AI that sits on your real workflows.
AI copilots and document workflows for teams under 50 staff.
What the assessment covers
Map where AI would actually save time or make money
Rank opportunities by impact vs. effort in a written report
Honest go/no-go — we'll say when AI isn't the right fix
A build, end to end
A first build often looks like this: quoting rules, product specifications, and warranty terms live across a shared drive, several PDFs, and one long-serving staff member everybody interrupts.
- Agree the question set the tool must answer, and the documents that are actually authoritative for each one.
- Connect those documents as the only source the assistant may draw on, so it cannot invent a specification.
- Test it against real questions your team asked last month, and compare the answers to what the expert would have said.
- Put it where the work already happens — the inbox or the tool the team has open — rather than behind another login.
Outcome: Specification questions get answered without interrupting the one person who knows, and every answer cites the document behind it — so when a rule changes you update one file instead of retraining people.
What we build
In-tool helpers, ask-your-files bots, lead sorting, AI-assisted quoting
A typical first build is an answer bot over your own documents: quoting rules, product specs, warranty terms, whatever your team currently asks the one person who knows. It sits in the tool they already have open, cites the source document, and says it does not know rather than inventing an answer. Others we have scoped include inbound enquiry classification that routes to the right person with a drafted first reply, and quote drafting that pre-fills from historical jobs for a human to check and send.
How a build runs
Timings assume you can give us a few hours of subject-matter time in the first and third weeks. The evaluation step is the one most projects skip and the reason most AI pilots quietly stall.
Discovery & Opportunities Assessment45 minutes
Map where AI would genuinely save time, and rule out the tasks that need a deterministic flow instead.
Data and accessWeek 1
Agree which documents and systems are authoritative, and get read access without opening anything you should not.
Build and evaluateWeeks 2-4
Wire the model to your data, then score its answers against questions you already know the answer to.
Pilot with one teamWeeks 4-6
One group uses it for real work while we watch failure cases and tighten the prompts and sources.
What it costs and how we scope it
AI work is quoted at a fixed price after the Discovery & Opportunities Assessment, because the cost driver is rarely the model — it is the plumbing to your data and the evaluation work to prove the output is reliable enough to trust. The assessment produces a ranked list with rough effort against impact so you can see what the second and third builds would cost before committing to the first. Ongoing model usage is billed at cost and is usually small relative to the build.
Every build starts with a Discovery & Opportunities Assessment so you know the fixed price before work begins. Take the free Operations Readiness Scorecard if you want a quick read on priorities first.
When this is the wrong fix
AI is the wrong fix when you need the same answer every time. Approvals, compliance checks, pricing rules, and anything auditable should be a deterministic flow, not a language model. It is also the wrong first purchase if your underlying data is scattered across three systems and nobody agrees which one is correct — clean that up and the AI build gets cheaper and better. We will tell you when that is the case rather than sell the more interesting project.
Related reading
FAQ
- Do you train models from scratch?
- Rarely. We use proven models and focus on wiring them into your tools and workflows.
- Is AI always the right answer?
- No. Sometimes you want the same result every time — a checklist, a fixed flow, a plain tool. We use AI when it helps; when repeatability is better, we build that instead.
- Will we own what you build?
- Yes. Deliverables are yours — code, configs, and documentation included.
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