Post-launch

Managed AI operations

What happens after deployment: monitoring, cost governance, and SLA-backed support for the systems we ship.

Length Ongoing retainer

Tier Post-launch

Stack SLA-backed

What managed operations includes

Ongoing operation of the systems we deploy, under an SLA, so they keep performing after launch.

01 Ongoing model monitoring and performance tuning

02 Cost governance and cloud spend optimization over time

03 Retraining and refresh cycles as data and models drift

04 SLA-backed support for deployed AI systems

Built for teams who want the system run, not just shipped

Who it is for CTOs, CIOs, and operations leads, post go-live

Engagement Ongoing retainer

Where it fits The retention and expansion layer, introduced after a first engagement closes

We run production systems, we do not disappear after launch. Managed operations positions Quantinev as a long-term infrastructure partner, not a project vendor.

Proof point · strategy §2

Where this usually sits in a wider engagement.

Generative AI and agents The build that comes before the retainer See the pillar AI-native product engineering Operate the product we built, after launch See the pillar

Have a workflow in mind already?

Bring it to the readiness assessment. Two weeks, fixed scope, and it ends with the use case costed rather than admired.

You leave with a costed plan you can act on, whether or not you build it with us.

What you leave with

A prioritised list of AI use cases, scored on value and effortA costed plan for the top one, with a delivery dateThe architecture it would need, on the stack you already runAn honest read on what is not worth doing yet Book an assessment No pricing games. We scope it on the call.