ENTERPRISE AI TRANSFORMATION
Enterprise AI transformation services
Ambition and pilots are only the start. Strategy, engineering and governance turn them into systems the business can rely on, without stopping operations to get there.
- A costed roadmap in two weeks, not an eighty-slide theory deck.
- Agents that reach production because governance is built in, not added after.
- Client-facing teams in Australia and the United States, supported by our engineering base in India.
Three things decide whether AI reaches production.
Not the model. The model is the part that already works.
Know which use case earns the budget
A scored baseline across strategy, data, platform, talent and governance, and a use-case backlog ranked on business value against technical feasibility. The output is a decision you can fund, not a list of ideas.
Agents that survive contact with your systems
Agents grounded in your systems of record through a governed access path, with the autonomy line set per use case rather than once for everything. The integration boundary is where otherwise promising programmes can stall.
Governance you can point at, not policy you hope for
Work is proposed, independently checked against the standard it has to meet, and cleared by a person with the standing to refuse it.
Four reasons enterprises pick us over a strategy firm.
Move from demonstrations to governed outcomes
We prove a use case on one real process with an owner and a before baseline, then let the evidence decide how far it goes.
Engineering depth since 2008
AI sits on systems that have to keep running. We have been building and operating enterprise software since 2008.
Your cloud, your models
The work runs inside your own tenancy, on the model families you have already approved. You are not buying a dependency on ours.
Local and global
Client-facing teams in Australia and the United States work with our engineering base in India, creating practical overlap across regions.
Start where the risk is lowest.
Three ways in. Start with the assessment when the outcome, evidence or first investment is still uncertain.
AI Readiness Assessment
An outside read on where you actually stand, before you commit a budget. Two weeks, fixed fee.
Build and integrate
One process, taken from approved requirement to governed production.
Managed AI
Ongoing ownership after go-live keeps the system reliable, observable and ready to change.
From a first call to something running.
AI Readiness Assessment
Where you stand across data, platform, talent and governance, scored rather than described.
Pick the one process
One real process with a named owner and a measurable before state. Ambition comes later.
Build it governed
Specification, plan and release each signed by a person, with the blast radius mapped first.
Show the evidence
What was produced, what checked it, what it cost, measured against how the work ran before.
Extend where it holds
The pattern moves to adjacent work where the evidence supports it, and stops where it does not.
Rated by the teams we build AI for.
ERP modernisation at distribution scale
Blackwoods moved core processes to Dynamics 365 through a phased programme planned around supply-chain continuity, reporting near real-time visibility across more than 400,000 SKUs.
Scaling digital operations for a national QSR
At Hungry Jack's, Softobiz supports the connected data, platform and operational engineering around the restaurant environment.
Score your own agent in three minutes
Ten questions across the five dimensions that decide whether an agent is production ready.
What leaders ask on the first call.
The AI Readiness Assessment runs in two weeks on a fixed fee, and it needs a handful of interviews rather than a data-readiness project first. It produces a baseline and a costed roadmap for a first build.
The assessment tests the first use case, the supporting data and the production foundations before another build is funded. It makes those gaps visible while they are still inexpensive to address.
Almost never. The blocker is usually a governed way in and out of the systems you already run, not the systems themselves. We start read-only against your systems of record and treat write access as a separate decision with a named approver.
You do, throughout. That is a deliberate structure rather than a stage in a handover plan, and it holds whether we build one use case or run a standing function for you.
Someone has to own cost, drift and incidents, and if nobody is named for it the programme decays quietly. That work sits in managed AI, and we would rather scope it up front than have you discover the gap in month six.
Bring the pilot that stalled. We will tell you why.
Two weeks, fixed fee, and an outside read you can take to a board. If the answer is that you are not ready to build yet, we will say that.