Discovery and feasibility Several directions tested cheaply.
We run fast, reversible experiments before anything is committed. The baseline is measured here, in your own numbers.
AI Enablement Services
Digizoo helps delivery teams ship faster with AI, with quality you can prove.
We embed a small AI squad in your delivery teams for six months. Together we take real backlog items to production through an AI-enabled lifecycle, build the pipeline and guardrails your risk function can sign, and hand it all to your people to run.
What we do
Quality first, then speed
Most delivery teams already have AI tools. In most of them, output rises, review clogs and trust falls, because the way of working never changed.
We embed a small squad for six months, agree the quality bar before speed is asked for, build the pipeline that produces evidence as it runs, and take real backlog items to production with your people. Then they run it without us.
The challenge
Three things decide whether AI adoption works
We organise the whole engagement around them.
How we help
Six gates from idea to production
Each backlog item climbs them in order, and a named person owns each gate.
Problem framing and value The commitment gate.
Each item gets a problem statement, a value hypothesis and a risk class. That decides whether the expensive work happens at all.
MVP build A working build in a non-production environment.
The agentic pipeline does the routine work and your developers direct it. Each step produces its evidence as it runs.
Quality, risk and trust Guardrails signed off as a gate.
We run evals against golden sets and regression suites, and build the controls your risk function asked for, mapped to their requirements.
Hardening and release Through your CI/CD and your release authority.
We write the playbooks, set up the monitoring and run the incident process. The pipeline never becomes a second path to production.
Measure and iterate Compared with the baseline.
We measure cycle time, defect rate, change failure rate and eval pass rates. The result earns the next item.
How it compares
Three ways to bring AI into delivery
| An embedded squad | Roll out the tools | Add people to the project | |
|---|---|---|---|
| Real backlog items reach production during the engagement | Yes | No | Yes |
| A measured baseline before any gain is claimed | Yes | No | No |
| Quality gates run on evidence, not status reports | Yes | No | No |
| Review is redesigned as the work shifts to judgement | Yes | No | No |
| Guardrails your risk function signs off as gates | Yes | No | No |
| Your own people run it afterwards | Yes | Yes | No |
Proof
We've done this before
The pipeline and the quality approach have each been proven on live work, in regulated environments.
A New Zealand customer-owned bank
A board question answered with evidence, four months before go-live
Halfway through a core banking replacement, the board asked what the test coverage was and nobody could answer with evidence. We scored risk on the bank's own framework, made evidence a by-product of the work and ran the go and no-go decisions on it. The first rigorous measurement put the payments baseline at around 18%, early enough to act on. The bank kept a reusable QA and AI framework that runs without us.
“You were coming with fresh eyes. You've got that independence.”
Delivery Advisor on the engagement
Ark, our own product
The pipeline runs in a regulated bank's production today
Read more about ArkThe agentic pipeline we would build with you is the one we run on our own backlog. Ark ships as deployable images into a regulated mutual bank's production estate, and time to ship has gone from quarters to a sprint. Every skill in the pipeline produces evidence against a real delivered item.
How we work
Six months, in three phases
We run it alongside your people and hand capability over as we go rather than at the end.
Discovery and mobilisation
In the first six weeks we scope your first items with your portfolio owners, engage your risk function by name, stand up the pipeline and measure the baseline. The lifecycle is drafted with your people, not handed to them.
Co-build and deliver
At least two backlog items go all the way to production. Capability and assets are handed over as we go, in demonstrations and retrospectives, rather than at the end.
Handover
Your squad takes the next item through the lifecycle with us out of the room. You keep the pipeline, the versioned skills, the risk and controls register, the runbooks and the blueprint the next squad works from.
Outcomes
Measured against your own numbers
6
months, embedded
Discovery, co-build and handover, run alongside your own people.
2+
backlog items in production
Taken through the full lifecycle during the engagement, then a third by your squad alone.
6
gates with named owners
Each one run on recorded evidence rather than narrative status.
FAQ
Questions we get asked
Where has this run in production?
The pipeline runs today on our own product, Ark, which ships into a regulated mutual bank's production estate, and the quality approach ran on a core banking replacement at a New Zealand bank.
Our estate is brownfield. Where does the gain actually sit?
It does not sit in deploying more often, because release authority stays yours and your cadence does not change. It sits earlier: in requirements and specification preparation, test design, the evidence assembled around it, and the review and approval step itself once evals make it fast.
How does this fit our chapters and squads?
Chapters own the standard and squads pass the gates. The lifecycle becomes a chapter craft standard, one baseline rather than many, and the gates are passed in each squad's existing cadence by whoever already holds the accountability. We facilitate, hand it to the chapter, and step back.
How does this meet our risk function and the regulator?
We build guardrails as mechanism and sign them off as gates. Each agent gets one objective and structured hand-offs. Lane isolation stops a testing agent reaching development secrets, stopping conditions halt a run that goes wrong, and the audit log records who approved what and in what context. We engage your second line by name in week one and map the controls to their requirements.
How is capability transferred, and how do we know it worked?
We transfer it by pairing on real work, against competency checkpoints we agree at the start. The handover pack stands on its own: an ownership matrix, runbooks and an offboarding pack built as we go. The test is that your squad takes the next production item through the lifecycle without us.
What tools do you use?
We use yours, wherever you have them. The pipeline runs inside your enterprise AI environment on your managed infrastructure, integrates with your existing CI/CD, and its skills reach the systems your teams already work in. It is designed to keep working without our harness.
Get in touch
Ready to make AI part of how you deliver?
Tell us where AI sits in your delivery today and what your board or risk function has asked about it.