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Cloud & Managed AI Operations

MTK keeps data platforms, models and agents secure, reliable and improving after go-live, under agreed service levels.

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A live operations console tracks four service levels; model quality drifts out of the agreed band, the team responds and improves it, and a service report is producedA live operations console tracks four service levels; model quality drifts out of the agreed band, the team responds and improves it, and a service report is produced

The challenge

What happens to AI after launch

  • Models drift

    Forecasts and agents lose accuracy as data, prices and customer behaviour change, often without anyone noticing until results suffer.

    Addressed byManaged data and AI operations

  • Costs grow unseen

    Cloud and model usage costs rise with adoption when nobody tracks them by use case or business unit.

    Addressed byCloud foundation

  • Security and audit gaps

    Access, logging and model risk controls are added late, if at all, which makes audits slow and incidents hard to trace.

    Addressed byAI security and compliance

How it works

An operations loop after go-live

Models, data and agents are monitored, corrected and improved continuously, and reported against service levels agreed with the client.

  1. Monitor
  2. Detect
  3. Respond
  4. Improve
  5. Report

Detects

  • Drift
  • Cost
  • Incidents

Agreed service levels

  • Availability
  • Incident response
  • Data freshness
  • Model quality

In the client's cloud, with access the client can revoke

What MTK offers

Cloud and operations services

  1. Cloud foundation

    Secure landing zones, identity, networking and cost controls for data and AI workloads, set up in the client's cloud subscription.

    OutputEvery component documented and handed to the client's IT team, so nothing depends on knowledge held only by MTK.

  2. Managed data and AI operations

    Monitoring, incident response, retraining and improvement for pipelines, models and agents under agreed service levels.

    OutputRegular reports on availability, model quality, usage and cost, with a backlog of next use cases.

  3. AI security and compliance

    Access control, audit trails, model risk controls and human oversight records for regulated and high-impact use.

    OutputControls that follow the client's policies and applicable AI regulation, reviewed when either changes.

How MTK delivers

From handover to steady operations

Operations run on the same platform used to build the solution, so monitoring and governance are part of it from the start.

Stages

  1. Assess

    Current workloads, controls and costs reviewed against target levels.

  2. Plan

    Landing zone design, service levels and operating runbooks.

  3. Pilot

    One workload moved under MTK monitoring and support.

  4. Scale

    Further workloads onboarded with the same controls and reporting.

  5. Operate

    Monitoring, reporting and continuous improvement against service levels.

  • Monitoring

    Model quality, data freshness and agent activity tracked in one place, with alerts routed to the support team.

    Pilot · Operate

  • Audit trail

    Every AI proposal, approval and data change recorded and traceable for internal and external review.

    Scale · Operate

  • Usage and cost

    Adoption and running cost reported by use case, so budgets follow the value each one delivers.

    Operate

Related use cases

Use cases kept in service

View all use cases

  • MTK Intelligence · Advisory

    AI governance starter

    A use-case register, AI policy, evaluation tests and decision log, kept current as new use cases go live.

    AI governance starter

  • MTK Energy

    Energy anomaly detection

    Consumption that departs from normal patterns flagged for action, with each alert tracked to resolution.

    Energy anomaly detection

  • MTK Predictive

    Demand forecasting

    Forecast error tracked against actual sales each cycle, with retraining when accuracy falls below the agreed level.

    Demand forecasting

FAQ

Questions about managed AI operations

Can MTK operate solutions it did not build?
Yes, after a review of the architecture, code and controls. Gaps found in the review are either fixed or agreed with the client before service levels start.
What do service levels cover?
Availability, incident response times, data freshness and model quality thresholds, agreed for each solution and reported on a schedule set with the client. Each report also lists open risks and planned improvements.
Where does our data stay?
In the client's own cloud environment. MTK operates with access the client grants and can revoke at any time, and every action taken is logged.
How does a solution move into managed operations?
Through a handover period in which the project team and the operations team run the solution together. Runbooks, monitoring and service levels are agreed before the project team steps back.
How are AI security and compliance handled?
Access control, audit trails and human approval for high-impact decisions are part of the design. They follow the client's policies and applicable law, and are reviewed when either changes.

A review of your AI operations

MTK reviews current workloads, controls and costs, then proposes service levels that fit how the business runs.

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Next in the lifecycleAI Strategy & Transformation — the next wave of use cases