Managed Hybrid AI
Managed support for hybrid AI systems
We monitor your deployed AI, rerun its test cases whenever models, prompts, or source information change, respond to incidents, and ship changes as tested releases. Coverage, service hours, responsibilities, and costs are agreed before onboarding.
Support hours and response targets are set in the service agreement.
The operating loop behind a supported system
Illustration
Monitor
Output quality, integrations, usage, and cost against the agreed measures.
Evaluate
Test cases rerun as models, prompts, and source information change.
Controlled release
Prompt, model, tool, and integration changes ship as tested releases.
Report
Quality, usage, cost, and exceptions for business, IT, and finance.
Improve
Fixes and enhancements prioritized from operating results, then the loop repeats.
Coverage, response targets, and included changes are set in the service agreement.
What we manage
A managed service needs clear responsibilities for both the application and its AI behavior.
Output quality
Maintain test cases and check output quality as models, prompts, and source information change.
Incidents and support
Agree who responds, what is covered, and when incidents escalate.
Performance and cost
Track response time, usage, and the cost of each unit of work.
Controlled releases
Treat prompt, model, tool, and integration changes as tested releases.
Access and data handling
Review model destinations, information access, tool permissions, approvals, and action records.
Planned improvements
Prioritize fixes and enhancements using operating results and business needs.
Review, support, or improve the system
System review
Review the system, establish operating measures, and prioritize the work needed.
- Workflow inventory and operating baseline
- Evaluation plan and agreed quality measures
- Usage, cost and integration review
- Prioritized improvement recommendations
Coverage, response targets, included changes, and pricing are agreed after a system review.
Reports for business, IT, and finance
Business results
- Throughput, cycle time, and quality compared with the starting measures.
- Manual review, exceptions, and remaining bottlenecks.
- Measured benefits and the assumptions used to calculate them.
Example reporting scope. We agree the measures, retention period, tools, and review schedule for your system.
Review before taking on support
Assess the architecture, dependencies, test coverage, permissions, and operating cost. Identify the changes needed before we can support the system.
01 / Review
System, data, controls, ownership
02 / Prioritize
Gaps, risks, and prerequisites
03 / Stabilize
Approved remediation and baseline
04 / Operate
Defined scope and service measures
Support for third-party systems depends on access, licensing, maintainability, and the work needed to stabilize them.
Agree the support terms upfront
How are service targets defined?
Agree measures for the specific workflow: availability, evaluation results, latency, cost, and human review rates. Support hours and response targets belong in the scoped service agreement.
How do changes affect data privacy?
Model, integration, and access changes are checked against the agreed data policy. Local-only workloads retain their no-cloud-fallback rule. Logs, storage, and external connections are documented separately.
What if another team built the system?
Start with a readiness review. Check source access, licenses, monitoring, permissions, and the effort needed to make the system supportable.
How does this connect with consulting?
Consulting develops the plan. Engineering builds and tests the system. Managed Hybrid AI supports it under an agreed service scope.
Keep the option to move support in-house
Agree the documentation, test cases, runbooks, operating history, data responsibilities, and transition work your team will need.
Discuss managed private AI
Tell us about the system, who uses it, and the support you need.