Performance monitoring
Track workflow quality, model behavior, errors, exceptions, latency, cost, and other operating measures relevant to the system.
Operate & Improve
Managed AI Operations
Ongoing engineering and operational support for production AI systems as models, workflows, data, integrations, and business requirements change.
Starting investment: $10,000+/month, typically with a 3-month minimum
Production AI is an operating system, not a one-time deployment
AI systems require ongoing evaluation, exception analysis, prompt and model tuning, integration maintenance, governance updates, and workflow refinement. Managed AI Operations gives your team continued access to the engineering and operational expertise needed to keep a deployed system useful, reliable, controlled, and aligned with changing business requirements.
Good fit when
- ✓You have a production AI workflow that needs active monitoring and improvement.
- ✓Your internal team does not want to own every model, prompt, integration, evaluation, and exception issue.
- ✓The workflow is business-critical enough to justify defined operating ownership and response expectations.
- ✓You expect ongoing expansion into adjacent workflows or additional integrations.
- ✓Governance, permissions, models, data, or business rules change frequently enough to require continuous maintenance.
What this engagement creates
Outcomes built around your operating environment
Evaluation and quality review
Maintain representative test sets and evaluation routines so changes can be assessed against defined acceptance criteria.
Optimization
Improve prompts, retrieval, routing, model selection, orchestration, and business logic as usage patterns and requirements become clearer.
Integration maintenance
Maintain and adapt connections to upstream and downstream systems as APIs, schemas, permissions, and operating processes change.
Governance updates
Adjust human-review thresholds, permissions, logging, escalation rules, approved knowledge, and control policies over time.
Ongoing engineering
Prioritize and implement incremental enhancements, workflow extensions, and new capabilities without restarting a separate project for every change.
Typical deliverables
What you can expect
- ✓Defined operating cadence and ownership model
- ✓Monitoring and evaluation routines
- ✓Exception and quality review
- ✓Prompt, model, retrieval, and orchestration optimization
- ✓Integration and dependency maintenance
- ✓Governance and control updates
- ✓Prioritized enhancement backlog
- ✓Ongoing engineering capacity and production support
How we work
1
Establish the operating baseline
Define system health, quality, cost, usage, exception, and business measures that matter for the deployed workflow.
2
Review and prioritize
Use operating data and stakeholder feedback to identify the highest-value reliability, quality, governance, and feature improvements.
3
Improve continuously
Implement controlled changes to models, prompts, integrations, retrieval, business logic, controls, and user experience.
4
Extend deliberately
Expand into adjacent workflows or capabilities when production evidence shows a compelling business case.
Keep ownership aligned with business criticality
Managed engagements are scoped around the system's importance, operating complexity, expected engineering capacity, support requirements, and service levels rather than a generic maintenance package.
Schedule a consultation