Managed AI Ops - We keep live AI healthy

Managed AI Ops is Platform Partner for AI already in production. The monthly agreement keeps it healthy. New features are fixed-price change orders.

Something already live

Tell us which AI nobody owns

A system we built, or one you inherited. We say what the monthly care includes, and what the first fixed-price order would be if the eval set is missing.

In the agreement - What the retainer covers

Care of systems that are already live, whether we built them or we inherited them.

Evals on a cadence. The golden set runs on a schedule, not only when someone remembers to check.
Regression on model and prompt changes. A change that fails the set does not go out. The gate sits in CI.
Drift, latency and cost. Alerts when answers move, when it gets slow, or when the bill moves.
Incident response. A person from us when the system is wrong in production. We write down what happened and what we changed.
A monthly report. What ran, what failed the eval set, what it cost, and what we recommend next. The next feature is not started from the report until you approve a price.

How the agreement works

Same commercial shape as Platform Partner. The retainer is care. Features are separate.

1. Start from the system you have

Aidoni-built or inherited. We learn the prompts, the data boundary and the eval set. If there is no golden set, writing one is the first fixed-price order.

2. Monthly care

Evals, regression, monitoring, incidents and the report. Small fixes that keep it healthy sit inside the agreement. We do not start new features from the retainer.

3. New features are change orders

A new tool, a new source or a new workflow gets a fixed price before we start. Nothing outside the agreement until you have approved it.

See Platform Partner

Care, with a clear edge

You should know what the monthly agreement includes, and what it does not.

The split is explicit: The retainer keeps it healthy. New features are fixed-price change orders. Same language as Platform Partner.
Eval-native: If the system has no golden set, that set is the first thing we put in place. Later changes have to pass it.
Your environment: We operate inside the boundary you already have, on AWS, Microsoft Azure, Google Cloud, GleSYS, or on-prem. Data stays there. Audit logs stay on. We do not train on your data unless you ask us to, in writing.

Related - The platform, the sprint, the team

Managed AI Ops is the care package. The others are how a system gets built, or how a person sits in your team.

Full-service

Platform Partner

Takeover of an app, portal or connected product: platform check, monthly care, then fixed-price AI builds.

Fixed project

Hephaestus Sprint

When the AI is not live yet. One system in four to six weeks, then this agreement if you want us to keep it.

Engineering teams

AI Coding Rollout

When the live system is how your engineers use coding agents, and the gap is the team setup.

Consulting

Forward-deployed engineers

A senior in your team when care is not enough and you want someone building with you.

All AI packages

How the sprint, this retainer and the coding rollout fit together.

Related insights - Keeping AI in production

What has to be true before a system should be scaled, and how to watch it.

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FAQ - Common questions

The same questions we get about Platform Partner, scoped to AI.

What is in the retainer, and what is a change order?

The retainer keeps the system healthy: evals, regression on model and prompt changes, drift, latency and cost, incident response and a monthly report. A new feature, a new data source or a new workflow is a change order at a fixed price, approved before we start.

What does it cost?

A monthly agreement, priced before we start. We do not publish the amount. It follows how many systems are in care and how they are hosted. Change orders are priced one by one.

Does Aidoni have to have built it?

No. Inherited systems are a normal start. If what you have is an app, a portal or a connected product with no owner, start with Platform Partner. This page is the AI on top of a system.

What if there is no eval set?

Then the first order is to write the golden set and put the CI gate in. We do not pretend to operate a system we cannot tell has got worse.

Where does the data stay?

In your environment, on AWS, Microsoft Azure, Google Cloud, GleSYS, or on-prem, with audit logs. We do not train on your data unless you ask us to, in writing.

Ready to talk?

Bring the system, not a maturity model

We will tell you straight if it needs care, a sprint, or a platform takeover first.

Let's talk about what you're building

The Aidoni team together outdoors in Gothenburg