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Operate · Service 08

ManagedAIServices. KeepAIinproductionperforming,improving,andaccountable.

A Managed AI Services engagement runs the operational layer behind your AI agents and automations. Monitoring, evaluation, tuning, vendor management, and incident response. Australian-based team, defined SLAs, and reporting your finance and risk functions can rely on.

Australian-based operations team
Defined SLAs and reporting
Accountable to outcomes, not effort

What you get

AI in production, properly run. Owned outcomes, not just uptime.

A managed service tier that keeps your AI agents and automations performing against the metrics they were built for. Defined scope, defined SLAs, defined reporting cadence.

Monitoring & Observability

Active monitoring of quality, latency, throughput, exception rate, and cost per call. Alerts wired into your existing channels. We see issues before your customers do.

Evaluation & Quality Tuning

Regular evaluation runs against your real data, with tuning cycles to address regressions, drift, and edge cases. Quality is measured monthly, not assumed.

Vendor & Model Management

We manage the underlying model and platform vendors on your behalf. Pricing changes, model updates, deprecations, and capability shifts handled without your team carrying the load.

Incident Response

Defined response model for AI incidents covering quality regressions, vendor outages, data issues, and customer complaints. Clear ownership and a documented runbook.

Improvement Backlog

An ongoing backlog of tuning, integration, and capability improvements driven by real usage data. Prioritised with you each quarter, delivered against the agreed budget.

Executive Reporting

Monthly and quarterly reports covering quality, cost, hours saved, and improvement progress. Built for board and finance review, not for show.

How it works

A defined operating model. On from day one.

Managed services are scoped against the AI assets you have in production. Onboarding is fast, ownership is clear, and reporting starts immediately.

Week 1

Onboarding & Inventory

We document every AI asset in scope, the metrics that matter, the runbooks in place, and the integration surface. Output: a single-page operating brief signed off by both sides.

Week 1–2

Monitoring & Alerting Setup

Monitoring, evaluation, and alerting are wired into your channels. SLAs and on-call coverage activated. Reporting cadence set with finance and operations.

Ongoing

Quarterly Improvement Cadence

Each quarter we agree the improvement backlog with your team, deliver against it, and report on outcomes. Effort is bounded; outcomes are measured.

Ongoing

Incident & Vendor Management

Issues are triaged, owned, and resolved against agreed SLAs. Vendor changes and model updates are handled by us, not by your team.

Quarterly

Quarterly Business Review

Sixty-minute review with leadership covering performance, cost, hours saved, regulatory posture, and the next quarter's improvement priorities.

ROI focus

What it actually saves you.

AI in production is not a fire-and-forget asset. Models drift, vendors change, edge cases compound. A managed service is the cheapest way to keep the value compounding instead of leaking out.

0–40%
Improvement in production quality over twelve months

Active tuning and evaluation routinely lift production quality by twenty to forty percent inside the first year, against the launch baseline.

0–50%
Lower total cost of ownership

Consolidating monitoring, vendor management, and tuning under a single retainer is materially cheaper than building the same capability internally for a small AI footprint.

Defined SLA
Response on every incident

Incident response is contractual, not best-effort. Critical issues are owned and escalated against an agreed clock.

What you walk away with

  • Active monitoring across every AI asset in scope
  • Quality tuning that compounds, not decays
  • Vendor and model management handled for you
  • Incident response with defined ownership and SLAs
  • Reporting your board and finance team can rely on

Common questions

What people ask before they book.

No. Lightweight or low-stakes use cases can be self-managed. Managed services make sense once you have AI in production that customers depend on, that touches sensitive data, or that materially affects revenue or operations.

Next step

Keep AI in production performing.

A 30-minute Strategy Session is the right starting point. We will talk through your AI footprint, what is in production, and whether a Managed Services arrangement is the right next step. No vendor pitch. No obligation.