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Delivery Models

Ongoing Managed AI Support

Monitoring and maintaining an AI system after it ships — tracking accuracy drift, adjusting prompts or retraining as your data changes, fixing what breaks — instead of leaving a model to degrade quietly after launch.

Monitoring · Drift Detection · Ongoing Maintenance

What it is

An AI system's accuracy isn't fixed the day it ships — the data it sees changes, and without monitoring, that drift goes unnoticed until something breaks visibly. Managed AI support is the ongoing counterpart to a build: tracking accuracy, catching drift, and adjusting the system as your data and needs change.

How it works

  1. 01
    Establish an accuracy baseline

    What "working correctly" looks like at launch, so drift later has something concrete to be measured against.

  2. 02
    Monitor continuously

    Ongoing tracking of accuracy and confidence, with alerting when performance moves outside expected bounds.

  3. 03
    Adjust as data changes

    Prompt updates, retraining, or threshold adjustments as your real-world data shifts — not a static system frozen at launch.

  4. 04
    Fix what breaks

    A defined support path for issues surfaced by monitoring or by your team, not a black box you have to escalate blindly.

Benefits

  • Accuracy drift caught before it becomes a visible failure
  • A system that adapts as your data changes, not one frozen at launch
  • A defined support path instead of an unmaintained model

Frequently asked

How is this different from a warranty period after launch?

A warranty covers defects in what was built. Managed support is ongoing — actively monitoring and adjusting for drift that happens naturally as your real-world data evolves, which isn't a defect at all.

Can this cover a system you didn't originally build?

Yes, after an initial review to establish what the system does and what a healthy baseline looks like.

Not sure this is the right fit yet?

A scope call is a lower-commitment way to find out before anything gets built.

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