Scheduled Health Checks
Systems reviewed on a fixed cadence, not only when someone notices something looks wrong and raises it after the fact.
Watched after launch, not left running blind.
Overview
An AI system on launch day and the same system six months in are rarely the same thing. Your workflows shift, your data changes shape, edge cases turn up that nobody scoped for. A system left completely unattended drifts quietly until someone notices it has been wrong for weeks.
We build monitoring into every system we hand over: a fixed schedule for checking outputs against reality, a defined threshold for what counts as drift, and a tuning process for correcting it before it becomes a support ticket or, worse, a decision made on bad output. This is what keeps a system trustworthy long after the launch excitement wears off.
Where it fits
One part of team enablement
Each one can run on its own or as part of a wider engagement.
Live system
Your AI system
One your team trusts, understands and can extend
Structured handover
What it does, how it fails and who owns it
Team training
Live sessions on your real workflows
Playbooks + documentation
What to check and what to escalate
Governance + guardrails
What it can act on alone and what needs a human
This service
Ongoing monitoring + tuning
Drift and failures caught on a fixed schedule
Live support
A direct line back to the people who built it
Capabilities
What we deliver
Output Quality Sampling
Regular spot checks of what the system is actually producing against what a human would expect, catching quiet quality drift early.
Drift + Failure Alerts
Defined thresholds that flag when a system starts behaving outside its normal range, so problems surface before your team does.
Model + Prompt Tuning
Adjustments to prompts, logic and configuration as your workflows evolve, keeping the system aligned with how the business actually runs now.
Usage + Performance Reporting
Plain-English reporting on what a system did, how well it performed, and where it is worth investing further, not a raw logs dump.
Planned Review Cycles
A standing check-in to reassess whether guardrails, scope and autonomy still match how much the system has proven itself.
Use cases
Where it pays for itself
- 01
A system that was great at launch
Output quality has quietly slipped since go-live and nobody has been checking. Monitoring catches the slide before it costs you.
- 02
The business has changed since launch
New products, new processes, or new data mean the system's original tuning no longer fits. We adjust it to match reality.
- 03
Nobody owns it day to day
The system runs, but no one internally is watching it closely. A scheduled monitoring cadence fills that gap without hiring for it.
- 04
Proving it out for more autonomy
You want to expand what a system is trusted to do unattended. Performance reporting gives you the evidence to make that call.
Case studies
Live systems, real-world application
Autonomous publishing system
Content Pipeline
Researches trends, writes and edits an article, generates its imagery and publishes to two production sites, three mornings a week, unattended, to a fixed schedule. That reliability holds because the pipeline is checked on a set rhythm rather than assumed to be working, with output quality sampled before drift ever reaches a live page.
Consumer AI platform
AutoAppraise
A live valuation platform where a driver gets a structured AI-generated report on their car, from free report to paid unlock. Vehicle market data shifts constantly, so the report engine is reviewed and tuned on an ongoing basis to keep valuations accurate as real-world prices move.
FAQ
Frequently asked
01If the system was built properly, why does it need ongoing monitoring at all?
Because your business is not static. Data shapes shift, workflows change, new edge cases appear. A system tuned perfectly for launch day will drift from that fit over time even if nothing about the build itself was wrong.
02How do you know if a system has started drifting?
We define quality thresholds and check against them on a fixed schedule, output sampling, failure rates, and usage patterns. Drift shows up as a trend against those thresholds well before it becomes visible to your team.
03Is this the same as ongoing support for bugs?
It overlaps but is not the same thing. Support fixes something broken. Monitoring and tuning catches a system that still technically works but has started producing worse results, which is a much easier problem to miss.
04Can you take over monitoring for a system you didn't originally build?
Yes, provided we can get a clear enough picture of what it is meant to do. We set the same baseline checks and thresholds around it as we would our own build.
Start here
- What you get
- A straight answer on whether AI pays off in your business, and what it would take.
- Office
- 2 Wakefield Street, Auckland CBD, Auckland 1010, New Zealand
- Response time
- Within one working day
Start here
A system left unwatched will drift. Let's keep it honest.
30 minutes, no pitch deck, no obligation. Tell us what's eating your team's time and we'll tell you straight whether AI is the answer, and roughly what it would take.