Managed services for custom AI solutions on Azure
We keep what we build running: monitoring, tuning and iteration after go-live.
Overview
The failure mode for AI projects is rarely the build. It is month seven, when the content has moved on, nobody owns the prompt, quality has quietly drifted, and the system is still running up a bill. Managed service is the answer to that.
What it covers
Quality monitoring, not just uptime
An AI system can be fully available and still be answering badly. We track answer quality against a maintained evaluation set, so drift shows up as a number before it shows up as a complaint.
Content and index maintenance
Your documents change. We keep the index current, retire superseded sources, and make sure the system stops citing the policy that was replaced eighteen months ago.
Cost and usage review
Monthly reporting on consumption, cost per active user, and which workflows are actually being used — including the honest version, where something is not being used and should be cut.
Model and platform currency
Azure AI moves fast. We test new model versions against your evaluation set before migrating, so an upgrade is a measured improvement rather than a surprise.
How the engagement runs
A defined shape, so you know what happens next and what you get at each stage.
Response targets, review cadence, escalation path, and what is explicitly in and out of scope, written down before the service starts.
Application Insights and Azure Monitor wired to the things that matter — quality, latency, cost, and adoption — with alerting that reaches a person.
Ongoing monitoring, tuning, and a scheduled review where we bring the numbers and a recommendation for the next improvement.
What you end up with
A system that is still working, still current, and still worth its cost a year after launch — with someone accountable for keeping it that way.
Start with a free AI readiness assessment
Tell us the workflow. We'll tell you what AI can do with it.