How We Work

This isn’t your typical IT approach. This is SRE.

Under CPS 230 and the Financial Accountability Regime, your AI is now someone’s personal accountability. We build the monitoring and evidence that makes it defensible.

Schedule A Call

Eight years running 35,000+ systems at Google scale.

How we work

From exposure to operational confidence

Four stages. No bloat. Each one builds on the last — from understanding your current risk to running your AI reliability programme permanently.

1
Stage 01

Operational Audit

We map exactly where your AI stands and what’s missing

Details
2
Stage 02

Monitoring Framework

Visibility built for how AI actually fails, not how servers do

Details
3
Stage 03

Incident Playbook

Tested response protocols before you need them

Details
4
Stage 04

Ongoing Operations

Your reliability partner, not a one-time consultant

Details
Stage 01 — Operational Audit
Inventory of every AI system in production and who owns it
Failure-mode assessment across the six AI blind spots
Gap report scored against SRE practice, not vendor checklists
Prioritised remediation plan with effort and risk weighting
Outcome
You know precisely where your exposure sits — and can defend that answer to your board.
Stage 02 — Monitoring Framework
SLIs defined on model behaviour, not just infrastructure health
SLOs and error budgets agreed with the business, not imposed
Dashboards and alerting wired to on-call, tuned to cut noise
Drift, hallucination and cost signals captured from day one
Outcome
Degradation surfaces as a measured signal — before a customer reports it.
Stage 03 — Incident Playbook
Severity model and escalation paths specific to AI failure
Incident commander roles defined and rehearsed
Rollback, kill-switch and human-in-the-loop procedures tested
Blameless postmortem template and review cadence
Outcome
When it fails at 2am, your team executes a rehearsed plan instead of improvising.
Stage 04 — Ongoing Operations
Monthly reliability review against SLOs and error budget burn
Toil identified and automated out, quarter on quarter
Capacity and cost forecasting as usage grows
Evidence trail your auditors and regulators will accept
Outcome
Reliability becomes a standing operational discipline, not a project that ended.

Two Ways to Work Together

Enterprise AI deployment has a structural gap that most organisations don’t discover until they’re inside it. The technology arrives. The vendor moves on. And the business is left holding something powerful, expensive, and largely unmanaged.

PROJECT ENGAGEMENT

Operations Diagnostic & Build

A defined-scope engagement that delivers the operational framework your systems deployment is missing. Starts with a thorough audit, ends with a working system — monitoring, playbooks, ownership, and a team that knows how to use them.

  • Operations / AI Operations Audit — full landscape assessment
  • Monitoring framework design and implementation
  • Incident response playbook and ownership model
  • Workforce integration programme
  • Executive briefing and metrics baseline
  • Handover to internal team or ongoing retainer

ONGOING RETAINER

Reliability Partner

For organisations that want sustained operational expertise without building a full internal function. We become the reliability layer for your systems — AI resources, monitoring, responding, iterating, and reporting on an ongoing basis.

  • Continuous AI system monitoring and alerting (for AI projects)
  • Incident response on defined SLAs
  • Monthly performance and reliability reporting
  • Ongoing cultural and adoption support
  • Quarterly strategic review with leadership
  • Scales with your footprint as it grows
WHAT WE DO

The Operational Layer
Your AI Deployment Is Missing

AI Operationalisation is the discipline of making AI systems work in the real world, after go-live. Not the deployment. Not the vendor promise. The sustained, measurable performance of AI as a production system inside a living organisation.

It draws directly from Site Reliability Engineering — the methodology Google developed to keep mission-critical systems running at scale. We apply that discipline to your AI infrastructure, with the three pillars that enterprise deployments consistently lack.

1 — Visibility & Monitoring

Continuous visibility into AI system performance, output quality, and behavioural drift. Know what your AI is doing — and catch problems before your business does.

2 — Incident Response

A structured playbook for when things go wrong. Clear ownership, defined escalation, fast resolution. The same discipline that keeps global infrastructure running — applied to your AI systems.

 3 — Governance & Audit Trail

Continuous documentation, performance records, and regulatory evidence that answers the board question, the client question, and the regulator question — before any of them are asked. Not assembled after the fact. In place before it matters.

READY TO EXPERIENCE THE DIFFERENCE?

Let’s Talk About Your Ops

Clients notice. Your new AI tool is live. The clock is ticking. If you’re ready to talk to someone who’s operated at this level before — let’s have a conversation.

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Free 30-minute consultation • No obligation • Brisbane-based team