Quality is monitored continuously
Evaluations run on a schedule and on every change, against a set built from your real cases. Regressions are caught in a report, not in a complaint.
Reliability
AI systems degrade quietly. We watch so you do not have to
An AI system does not fail like a server. It keeps returning confident answers that are slowly getting worse - because a vendor changed a model, your product changed, or the questions people ask moved on. Without measurement, you find out from customers.
From$1,500/ month
Monitoring and alerting, scheduled evaluation runs, incident response and a quarterly review.
Indicative starting price. The fixed fee for your scope is quoted after the two-day diagnosis, before any build begins.
What makes it work
Three things we insist on
Drift is expected and managed
Input distributions shift, providers deprecate models, prompts decay. We track those signals and re-tune or re-ground before quality visibly drops.
Cost stays under control
Token and inference spend is tracked per workflow with alerting on anomalies, and we routinely move workloads to cheaper models where evaluation shows no quality loss.
Watch the inputs, not just the outputs Category mix, length distribution, retrieval scores, unknown rate. These move before accuracy does
Assert on the shape of the work Volume floors and freshness checks. A job that succeeds while doing nothing is the failure standard monitoring cannot see
Refresh the evaluation set quarterly Keep the old one too. Passing the old and failing the new is drift; failing both is degradation, and they need opposite responses
Someone reads it A named owner and a scheduled review. An evaluation nobody looks at is a recurring cost with no output
Capabilities
What is actually included
- 01
Monitoring and alerting
Latency, error rate, escalation rate, confidence distribution and spend, with thresholds that page someone.
- 02
Scheduled evaluation runs
Regression testing against your held-out set, reported with the deltas explained.
- 03
Incident response
Defined severity levels, response targets and a documented rollback path for every deployed system.
- 04
Model and dependency upgrades
Provider deprecations handled and new models evaluated on your workload before anything is switched.
- 05
Quarterly review
A session on what the systems delivered, what they cost, and what to change next quarter.
In detail
What this covers, specifically
A category name is not a scope. These are the individual pieces of work inside this practice - take the two that apply to you and ignore the rest.
- 01
Monitoring and alerting
Latency, error rate, escalation rate, confidence distribution and spend tracked continuously, with thresholds that actually page someone.
- 02
Scheduled evaluation runs
Regression testing against your held-out set on a cadence and on every change, reported with the deltas explained.
- 03
Drift detection
Watching for shifts in the questions being asked and the data coming in, so quality is corrected before anyone notices it slipping.
- 04
Prompt and config versioning
Every prompt and setting under version control with a diff history, so a quality change can be traced to the edit that caused it.
- 05
Incident response
Defined severity levels, response targets, a documented rollback path and a post-incident write-up for every production system.
- 06
Provider migration
Handling model deprecations and provider changes, with the replacement evaluated on your workload before anything is switched.
- 07
Cost tracking
Spend attributed per workflow with anomaly alerting, so a runaway loop is caught in hours rather than on the invoice.
- 08
Quarterly review
A session on what the systems delivered, what they cost, what broke, and what to change next quarter.
What you receive
Concrete artefacts, not a slide deck
- Monitoring and alerting across all production systems
- Scheduled evaluation runs with reporting
- Documented incident response and rollback plan
- Cost tracking and optimisation reviews
- Quarterly performance review
01 Diagnose Days 1-2
02 Prove Week 1
03 Integrate Weeks 2-3
04 Operate Ongoing
Around three weeks end to end. That comes from scoping tightly to one workflow - not from skipping a phase. Each still ends in evidence you can check.
Further reading
Written up in more detail
- The integration that silently stopped syncing A timeline of the failure nobody sees: what each person observed, why none of them connected it, and the three checks that would have caught it on day one.
- Why automations fail silently The automation that reports success while doing nothing is the expensive failure. Why status codes miss it, and the three assertions that catch it.
Let's talk
Put a number on how it is performing
Most teams cannot say whether their production AI is better or worse than it was six months ago. We will establish that baseline and stand up the monitoring that keeps the question answerable. If you have inherited something nobody understands, start with AI System Rescue instead.
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