Managed AI
Launch day was the easy part.
Month seven is where it slips.
Ongoing monitoring, maintenance, security and scaling for the AI systems you’ve deployed and the small-business stack they sit on. The aim is simple: notice the answers getting worse before a customer does.
- Deployed AI, whoever built itWhat we look afterWatching
- Quality you can see trendingWhat we look afterWatching
- Security around the modelWhat we look afterWatching
- A bill you can predictWhat we look afterWatching
- The small-business stack underneathWhat we look afterWatching
- Room to grow without a rebuildWhat we look afterWatching
Foothills Digital—Sentrello Premier Partner
- AWS
- Azure
- Google Cloud
- DigitalOcean
- On-premises Linux
- On-premises Windows
Every one of our people holds active certifications on the platforms they work on.
Sentrello is our only partnership. On the clouds, it's our people who hold the certifications. We don't claim a partner tier, and we'll keep saying so until that changes.
Where this usually starts
Nobody owns the AI once the project ends
The contractor who built it moved on. The invoice for the API calls didn’t.
- The provider retired the model version you built on, and the email about it went to an inbox nobody reads.
- Answers that were sharp in March have gone vague, and nobody can say when it started.
- Your usage bill doubled last month. Was that growth, or a loop?
- Someone pasted a customer list into a prompt, and there’s no record of where it went.
- Underneath it all sits one ageing server and a router nobody has logged into since it was installed.
What we do instead
We run it like production, because it is
AI fails in an unusual way. It rarely crashes; it just gets quietly worse. So we watch the signals that say it’s slipping, chiefly answer quality against a fixed test set and how often a person has to step in. Cost gets its own alarm. Automated checks catch most problems, and an engineer takes the rest, after which the fix becomes another check so it’s caught sooner next time. The stack underneath is patched and backed up like anything else we manage.
How it works
How we look after it
Five stages, and most of the work in them runs without anybody being paged.
Write down what’s actually running
Every model, prompt, agent, API key and data source, listed with an owner. Most businesses find at least one they’d forgotten about.
Set a baseline you can measure against
A fixed set of real questions with known good answers, run every week. A drop in quality then shows up as a number instead of a hunch.
Watch the spend and the answers
Alerts on usage spikes, on scores sliding below the baseline, and on anything reading data it shouldn’t. Software takes the first response, so it never waits for somebody to wake up.
Maintain it before it breaks
Model versions upgraded on our schedule rather than the provider’s, prompts re-tested after every change, dependencies kept current. Each change ships with a way back.
Scale it when the load says so
More users, more documents, a second location. We size the infrastructure to the traffic you have and grow it when the graphs say it’s time, not the sales pitch.
What we look after
Deployed AI, whoever built it
Chat assistants, agents, document pipelines, internal tools. Built by us, by a contractor or by your nephew over a long weekend, we take it on as it stands and document it as we go.
Quality you can see trending
A standing test set run on a schedule, with a score on a chart. When it dips we go and find out why before anybody gets a bad answer twice.
Security around the model
Prompt injection, leaked keys, data the assistant can reach but shouldn’t. Access is scoped tightly, keys rotate, and what goes into a prompt is logged.
A bill you can predict
Usage tracked per task and capped where that makes sense. A runaway loop costs you an alert instead of a month’s budget.
The small-business stack underneath
If the AI depends on it, it’s in scope. Laptops, the network, the cloud account and the backups, on AWS, Azure, Google Cloud, DigitalOcean or hardware in your own building.
Room to grow without a rebuild
When usage climbs we add capacity to what’s already there. Starting over because version one couldn’t stretch is the expensive outcome this is here to prevent.
Related
AI Agent & Workflow Automation
Customer questions, purchase orders, the quarterly tax pack. We design AI agents and automated pipelines that take that work off a person’s desk, and we wire them so you can see what they did and why.
Server & Infrastructure Management
Physical or virtual, Windows or Linux, in your building or in a cloud account, every server gets upgraded, patched and maintained by us. Patches roll out in rings, so the update that breaks something breaks the test box first.
Helpdesk & Tech Support
Remote or on-site help for whatever your staff are stuck on, from a frozen laptop to the app that won’t open after an update. Software catches and fixes most faults before anyone notices. The rest go to an engineer who already has the logs.
What have you got running?
The AI pieces you’ve deployed, who built them, and what they’re connected to.
Send us that and we’ll tell you what we’d watch first, and where the risk is sitting today.
- What we arePlan, create, launch, maintain
- What we areSentrello Premier Partner
- What we areEvery quote written for you