Services · Managed AI
An embedded AI owner for systems already moving into production.
Entry conditions
A real operating portfolio and people who own it.
- At least one live or approved production AI or automation system.
- An executive sponsor and named workflow, technical, data, and review owners.
- Access to the logs, source definitions, vendor accounts, runbooks, and outcome measures required for the agreed scope.
- A willingness to change workflows, train users, review exceptions, and maintain approved company context.
- A defined operating cadence and decision process for what expands, changes, pauses, or retires.
Not included by default
Bounded ownership, not an unlimited support promise.
- General IT help-desk, device, network, or unrelated application support.
- Unrestricted access to company data or permission to take consequential actions without approved controls.
- Guaranteed uptime, accuracy, savings, revenue, rankings, or business outcomes absent a specific evidence and service contract.
- New major builds, migrations, or integrations that have not been separately scoped and accepted.
- Silent changes to models, tools, prompts, permissions, or production workflows without testing and approval.
Operating scope
What ongoing ownership actually covers.
The signed scope names the systems, coverage window, response expectations, owners, evidence, and exclusions. These disciplines become the operating system around that scope.
Roadmap and portfolio ownership
Evaluation and quality monitoring
Reliability and incident coordination
Permissions and governance reviews
Cost and vendor management
Workflow and model change management
Team enablement
Leadership reporting
Operating cadence
Monitor continuously. Decide deliberately.
The exact cadence changes with business consequence and support terms. It should be explicit before the service begins.
Continuous / agreed coverage
Automated monitoring, alert triage, critical vendor notices, and issue tracking within the contracted service window.
Biweekly
Operational review of exceptions, quality, adoption, active changes, decisions, and blocked dependencies.
Monthly
Written system and business-measure report with source definitions, limitations, incidents, cost, and next actions.
Quarterly
Architecture, permissions, vendor, roadmap, and operating-model review; approve what to expand, change, pause, or retire.
Handoff and exit
The client must be able to understand, own, and transfer the system.
The operating record should include system inventory, architecture and data-flow diagrams, owners, access model, current configurations, vendor accounts, evaluation assets, incident history, runbooks, open risks, and roadmap decisions.
Offboarding terms should define notice, artifact export, account and credential transfer, documentation refresh, transition sessions, unresolved work, and secure removal of Krastor access. Client-controlled accounts and documented decisions reduce lock-in throughout the engagement—not only at exit.
Questions
Managed-service boundaries.
What are managed AI services?
They are the ongoing operating responsibilities required after an AI capability enters production: monitoring, evaluation, incident coordination, access reviews, model and vendor changes, cost control, documentation, training, reporting, and roadmap decisions.
How is this different from project implementation?
Implementation takes a bounded use case from decision to production. Managed AI services begin when approved systems need continuing ownership and an operating cadence. A business may use Krastor for one or both, but the scope and acceptance criteria remain separate.
What must already exist?
There should be at least one live or approved production implementation, an executive sponsor, named workflow and technical owners, access to required systems and evidence, and willingness to maintain the process—not only the software.
Is this an IT help desk?
No. Coverage applies only to the AI systems, workflows, vendors, and responsibilities named in the service agreement. General device, network, account, and unrelated application support remain with the client's IT provider unless explicitly scoped.
Do you guarantee uptime or business outcomes?
No blanket guarantee applies. Any service level, response target, or outcome measurement must be defined for the specific systems, dependencies, coverage window, and evidence available in the agreement.
Can we bring the work in-house or change providers?
Yes. Client-controlled accounts, current documentation, runbooks, configuration records, decision history, and an agreed transition process reduce dependency. Contract terms should define access transfer, exportable artifacts, credential rotation, and transition support.
Correct starting point
No production portfolio yet? Start with one bounded implementation.
The AI Opportunity Diagnostic identifies a viable first use case. AI Implementation & Agent Architecture owns the build-to-production path. Managed AI ownership begins when there is an approved system and operating responsibility to carry.