AI agents do the work. A named expert signs it off.
AI agents, an AI workforce, application modernisation and development, and business analysis for enterprise and government. AI use is disclosed on every deliverable.
Every option you have today is a compromise.
Your backlog is a capacity problem. The usual fixes each cost something you can't get back.
Months to recruit, months to ramp, and a fixed cost long after the project ends.
Rates built around hours, opaque delivery, and knowledge that walks out at handover.
The backlog compounds, the legacy system ages, and a competitor moves first.
A fleet of agents that starts quickly, scales with the work, and is priced against an outcome you agree first.
Five ways to put the fleet to work.
Pick the one closest to your pain. Prove it on a real outcome, then add the next.
AI agents
Purpose-built agents that take a defined workflow from trigger to finished result, inside your tools and your rules.
Learn more →AI workforce
A standing team of agents for ongoing digital work, there when you need it and off the budget when you don't.
Learn more →Application modernisation
Agents read the whole estate, map it, migrate it and test it, so modernisation stops being a multi-year freeze.
Learn more →Application development
Agents build, engineers review, tests prove it: software delivered quickly and to production standard.
Learn more →Business analysis
Requirements, process maps and options papers in days, grounded in your own documents and data.
Learn more →Something else?
If the work is digital and the output is code, data, text or documents, the fleet can probably take it. Tell us the outcome.
Describe it →Agents do the volume. People hold the judgement.
- Agents do the volume. Specialist agents research, build, test and document in parallel under one orchestrator.
- People hold the judgement. A named lead sets the scope, reviews every delivery and signs it off.
- Everything is logged. Each agent action leaves an audit trail you can inspect.
- Spend is capped. A hard ceiling per engagement, with cost and quality reported.
Deep platform practices behind the agents.
Agents are only as good as the platforms and governance around them. We bring both.
Google Cloud
Migration, architecture, security, data and AI on Google Cloud, delivered with agentic tooling.
Learn more →Google Workspace
Planning, migration, training and automation for Google Workspace, with AI built into daily work.
Learn more →Google Agentspace
Plan, deploy and tune Google Agentspace so agents are useful, integrated and governed.
Learn more →Planview
Portfolio governance and agent orchestration, designed around your operating model.
Learn more →Built for regulated, complex industries.
Built to be trusted with real work.
Powerful agents need firm guardrails. These are standing terms, not extras.
Disclosed by default
Every deliverable states what the agents did, what people checked and who signed it off.
Approved before we start
We get your written approval before we use AI on your work, and we document data handling per engagement.
Named and accountable
One named lead per engagement answers for the result. Contracts are with xShopper Pty Ltd, an Australian company.
Fixed scope, stated acceptance
Scope, acceptance criteria and a hard spend cap are agreed before work starts.
Straight answers to the questions buyers ask.
Who is accountable if an agent gets something wrong?
We are. Each engagement has one named lead who owns the result and signs every deliverable. The contract says so, and we fix errors at our cost.
Can agents get things wrong?
Yes. That is why every delivery has review gates, tests before handover and cited sources, and why we keep agents away from work that needs human judgement alone. We design the process so errors are caught before delivery. We do not promise zero errors.
Where does our data go?
Only where the engagement names. We document data handling per component before work starts, and we do not put personal or sensitive information into public AI tools. If your rules say certain data stays out of AI systems, it stays out.
How do we know this is not AI-washing?
Every deliverable says exactly what agents did and what people did. Start with a small fixed-scope pilot with written acceptance criteria, and judge the result.
What about lock-in and cost?
Scope and acceptance criteria are fixed before work starts, spend is capped, and everything we build is handed over in standard formats you can run without us.
Latest insights
What the DTA AI model clauses mean for suppliers who use agents
An annotated reading of the DTA AI Model Clauses notify-and-approve duty: what we do by default, and what buyers should ask suppliers who use agents.
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The six AI adoption practices, in a real delivery process
How the six practices in Australia's Guidance for AI Adoption map to concrete steps in an agentic engagement, with a short checklist.
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AI agents for enterprise: ten questions to ask any vendor, including us
Ten questions for buying AI agent services in Australia, with what a good answer looks like and where xShopper's answers are policy and where they…
Read article →Name the outcome. We'll bring the fleet.
Tell us the work you can't staff or finish. You get a fixed-scope proposal, written acceptance criteria, a hard spend cap and a named lead who answers for the result.
Request a scoping call