Your AI, run to a standard — for as long as it’s live.
Getting AI into production is the beginning. Keeping it performing — continuously, governed, and proven against the outcome — is the harder, ongoing discipline. We run it on your behalf, for as long as your AI is live.
Capability is everywhere. Operating it is the differentiator.
The models keep getting stronger and easier to reach. What separates AI that delivers from AI that drifts is whether someone runs it to a standard every day — watching it, governing it, correcting it, and improving it against a result the business cares about. We take that on as a managed service: we operate your AI so it performs, stays governed, and grows the outcome, for as long as it remains in production. Mid-market operators need this discipline most — they rarely staff a standing AI-operations function — yet the same service runs portfolios for private-equity sponsors and large institutions. Packaged as a product, the same operation is Sprout Operate.
The six surfaces we run.
Every production AI — whatever model sits underneath — comes down to six surfaces: what it may touch, what it may do, how it reasons, what it remembers, how it acts, and how it is proven. We run all six as one managed operation, monitored and governed to a standard that holds up in production, under audit, and in front of a board.
We run all six surfaces continuously — not as a one-time build, but for as long as your AI is in production.
What managed operations includes.
Continuous operation
We run your AI day to day — keeping it available, current, and performing against the result it was put into production to deliver.
Monitoring & governance
We watch the six surfaces, hold the controls, and keep an audit-ready record — so the system stays trustworthy and inspectable.
Performance to a standard
We operate to a defined standard and correct quickly when conditions, data, or models shift beneath it.
Optimization for outcome & cost
We tune the operation to grow the business outcome while holding the unit economics — what each result costs to produce.
A continuous loop, not a project.
Managed operations runs as a standing cycle. Each pass holds the standard and raises it.
Onboard
We take the AI into operation — the surfaces, the controls, the systems it touches, and the outcome it must deliver.
Operate
We run it day to day to a defined standard, keeping it available, current, and performing in real conditions.
Assure
We monitor the six surfaces, hold governance, and keep the record audit-ready and board-ready.
Optimize
We tune for outcome and cost, then feed what we learn back into the loop — so the operation compounds.
Scoped to your portfolio.
Tiers differ by the scope we operate, not by a fixed price. We size the engagement to what you are running in production today and where it is headed.
Production
We operate one process in production to a standard — the right starting point for an organization putting its first AI to work and wanting it run properly.
Scale
We run several processes together, with shared governance and a common standard across them — so growth does not fragment into disconnected deployments.
Enterprise
We operate a portfolio of AI across an organization or a sponsor's holdings — unified oversight, reporting, and optimization at portfolio scale.
Kept at the standard, on a loop.
Operate, assure, and optimize repeat without pause. The cycle is what keeps AI performing to standard as data, conditions, and models change beneath it.
Answers, up front.
What is Managed AI Operations?
Managed AI Operations is the discipline of running your AI systems in production as an ongoing service rather than handing them over after a build. We take operational responsibility for the live systems & the outcomes they are meant to produce, the way a managed service runs a function instead of selling you a tool. Operation is continuous because AI behavior, data, models & business conditions change — so operating it is a standing role, not a one-time project.
What does the managed service actually include?
It covers four things on a continuous basis: keeping the systems running in production, monitoring & governance so behavior stays observable & controlled, holding performance to an agreed standard, and ongoing optimization for both business outcome & cost. As models & usage shift, we tune, correct & re-govern rather than letting drift accumulate. The point is that someone runs the operation to a standard every day, not only when something breaks.
How is the engagement scoped?
Scope is set by the breadth of the AI portfolio under management, expressed in three tiers — Production, Scale & Enterprise. Production covers a focused set of systems, Scale covers a broader operation across more of the business, & Enterprise covers an organization-wide or multi-entity portfolio. We size the engagement to what you are actually running, so the commitment matches the surface area under management.
Who needs Managed AI Operations most?
It is built for organizations that depend on AI in production but cannot — or should not — stand up a full internal AI-operations function, which describes most mid-market companies. It is equally suited to private equity firms & institutional owners who need consistent, governed AI operations across a portfolio of companies. In both cases the need is the same: durable operational accountability without building the team from scratch.
Begin with a Charter.
A fixed-fee diagnostic that turns "we should use AI" into a costed, governed plan — and the on-ramp to running it as a managed operation. Start with the Charter, then we operate.