Quality monitoring
Continuous evaluation against golden sets with alerting when scores move outside tolerance.
Adoption & Value
Someone accountable on the Monday after go-live.
The problem we solve
AI systems drift: models change, content changes, usage changes. We run the ongoing evaluation, cost management and upgrade cycle under an SLA, so quality and unit economics improve rather than decay.
Continuous evaluation against golden sets with alerting when scores move outside tolerance.
Routing, caching and prompt optimisation reviewed against a published cost per resolved task.
New model versions benchmarked and canary-released so provider changes never surprise your users.
Defined severities, response times and rollback procedures for quality, safety and availability events.
A prioritised backlog of quality and efficiency work delivered each quarter.
How the engagement runs
System review, baselining, runbook capture and SLA agreement.
Monitoring, incident response and monthly reporting.
Quarterly review setting the next quarter's quality and cost backlog.
Common questions
Related practices
Role-based curricula, champion networks and adoption measurement that move teams from curiosity to daily discipline.
ViewProcess redesign, role impact mapping and workforce transition planning for the humans on the other side of automation.
ViewA benefit ledger tied to finance data, so claimed savings and realised savings are the same number.
ViewA 45-minute briefing with the people who would run the work — scope, timeline and a straight answer on whether it is the right next step.