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Adoption & Value

Managed AI Operations

Someone accountable on the Monday after go-live.

24/7
Monitoring with defined response
Quarterly
Quality and cost review
Tested
Model upgrades before they land

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.

What the work includes

Quality monitoring

Continuous evaluation against golden sets with alerting when scores move outside tolerance.

Cost management

Routing, caching and prompt optimisation reviewed against a published cost per resolved task.

Model upgrade management

New model versions benchmarked and canary-released so provider changes never surprise your users.

Incident response

Defined severities, response times and rollback procedures for quality, safety and availability events.

Continuous improvement

A prioritised backlog of quality and efficiency work delivered each quarter.

How the engagement runs

A sequence you can plan a quarter around.

  1. 01

    Onboard

    System review, baselining, runbook capture and SLA agreement.

  2. 02

    Operate

    Monitoring, incident response and monthly reporting.

  3. 03

    Improve

    Quarterly review setting the next quarter's quality and cost backlog.

Common questions

Before you commit.

Do you support systems you did not build?
Yes, after a short review to baseline quality, cost and operability.
Can we exit?
Ninety days' notice, with documentation, dashboards and runbooks handed over as standard.

Talk through managed ai operations.

A 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.

Book a briefing