Not technical maintenance
This is an operations capability, not a support contract. The question it answers is not 'is the server up' but 'is the system still producing the business result it was built to produce, and at what cost'.
Service
AI systems need an owner after launch.
Models change. Software changes. Data changes. Your business changes. A system that was accurate in March quietly stops being accurate in September unless somebody is watching it — managed AI operations is that function, outsourced.
Why systems drift
Underlying models are updated by their vendors. Integrated software changes its API or its fields. Your document set goes out of date. Volume grows. Staff change roles and permissions no longer match. None of this announces itself.
What is monitored
Output quality
Sampled outputs evaluated against the standard agreed at implementation, so degradation is caught by measurement rather than by complaint.
Errors and failures
Investigated, root-caused and fixed, with the fix documented.
Integrations
Watched for API changes, schema changes and authentication expiry — the most common cause of a system silently stopping.
Usage
Who is using it, how much, and whether adoption is where it needs to be for the business case to hold.
Cost
Consumption tracked against expectation, with alerts before an invoice becomes a surprise.
Access and permissions
Reviewed on a schedule, because entitlements drift as people join, move and leave.
What you receive
Monthly performance reports
What the system did, how well, what it cost, and what changed.
Workflow, model and prompt improvements
Continuous refinement rather than a system frozen at launch.
Employee support
The people using it have somewhere to take a problem.
Documentation updates
The written description of the system stays true as the system changes.
Quarterly opportunity reviews
The next workflow worth building, assessed with the benefit of everything the first one taught us.
Questions
Managed AI operations: common questions
What are managed AI services?
Managed AI services means an external team owns the ongoing operation of your deployed AI systems: monitoring output quality, investigating errors, watching integrations, usage and cost, reviewing access and permissions, supporting employees, reporting performance monthly, keeping documentation current, and improving the system over time. It is the function a business would otherwise have to hire for, and it exists because AI systems degrade without an owner in a way that conventional software does not.
Why can't we just maintain it ourselves?
You can, and some clients do — we document the system specifically so that is possible. It requires someone who will actually notice a gradual quality decline, understands why an output changed, and has time to act when an integration breaks. For many small and mid-sized businesses that person does not exist internally, and the system quietly stops being trusted rather than visibly failing.
What happens if the underlying AI model changes?
Model updates are one of the specific things managed operations exists to absorb. When a provider updates or retires a model, behaviour can shift on the same inputs. Because output quality is continuously evaluated against a baseline, that shift is detected as a measurement change, and prompts, retrieval or model selection are adjusted and re-tested before it affects your operations.
How is this priced?
Monthly, sized to the number and complexity of systems under management. We do not publish a fixed price, because the work required to keep one document-processing workflow reliable is not the work required to keep five interconnected systems reliable across three departments. It is quoted against a defined scope once the systems in question exist.
Can you manage AI systems we didn't build?
Sometimes, and it starts with an assessment rather than an assumption. We need to review how the system was built, what it connects to, what controls and documentation exist, and whether it can be monitored and evaluated in a meaningful way. Where a system was built without an operating layer — no monitoring, no evaluation, no error handling — the honest answer is often that adding one costs more than expected.
Get started
Find the first AI system worth building.
Show us where your business is losing time, revenue, capacity or visibility. We will help determine whether AI can solve it — and what the first practical step should be.
hello@askgeeks.ai · Vancouver, BC · remote across Canada & the US