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Managed Services & Operations
Operations that keep the business running
We run and support your applications, platforms and AI agents. Automation handles the routine, and site reliability engineers handle the exceptions, focused on your priorities and outcomes.
How we work
One accountable team from alert to fix, with AI agents triaging first
See the operating modelfrom idea to impact, across the systems lifecycle, this service sits here
Operating model
How work moves between automation, our engineers and your team
Pick an example and watch it travel. AI agents and automation handle the routine and prepare the evidence, our engineers make the fixes and your team decides anything with business impact.
Memory on the order service climbs in a pattern that matches a known leak.
Matches the pattern to an approved runbook and restarts the service one instance at a time, with no user impact.
Logs what happened and adds it to the weekly reliability report.
An engineer sees the repeat in the report and fixes the leak for good.
How priority is set
Priority combines business impact and urgency. Definitions and targets are agreed with you for each system during onboarding, and coverage hours follow the service tier.
- P1Critical
Business stopped, or customers cannot transact.
- First response
- 15 minutes
- Who acts first
- On-call engineers, backed by agent-driven triage brief.
- P2High
A key function is degraded for many users, with no workaround.
- First response
- 30 minutes
- Who acts first
- On-call engineers, backed by agent-driven triage brief.
- P3Moderate
Limited impact, or a workaround exists.
- First response
- 4 business hours
- Who acts first
- AI agent first, engineer if the runbook does not resolve it
- P4Low
Minor issue, question or request.
- First response
- 1 business day
- Who acts first
- AI agent or service desk
What we do
We keep the systems up and running
Applications, platforms and the AI now running inside them.
AI-accelerated
Application management and support
Day-to-day support with clear ownership, plus an improvement backlog that shrinks the support load over time.
Incident and problem management
Fast restoration, then root-cause fixes so issues stop coming back.
Enhancements and small changes
Improvements delivered through the same tested pipeline as projects.
Ticket intelligence
Incoming tickets classified, routed and often resolved automatically.
AI-acceleratedRunbook upkeep
Runbooks kept current from real incidents.
AI-acceleratedSite reliability engineering and monitoring
Full-stack observability that ties infrastructure health to what users experience.
Full-stack observability
Infrastructure, application and user-experience telemetry in one view.
Service levels and error budgets
Targets agreed with your business and tracked in the open.
Capacity and performance
Bottlenecks predicted and resolved before peaks.
AI-acceleratedOn-call and incident response
Clear escalation paths and blameless reviews.
AIOps, MLOps and self-healing
Machine learning on logs and metrics to spot trouble early, and automation to fix what has been seen before.
Anomaly detection
Unusual patterns flagged before they turn into outages.
AI-acceleratedSelf-healing runbooks
Known failure patterns resolved without waiting for a person.
AI-acceleratedModel operations
Deployed models retrained, versioned and watched for drift.
Change risk scoring
Risky releases flagged before they deploy.
AI-acceleratedAI agent monitoring and management
The same operational discipline for AI agents as for any production service.
Action and decision logs
Every tool call and decision recorded and searchable.
Cost and usage tracking
Spend per agent and per task, with alerts on spikes.
Escalation and override
Clear ways for people to step in, pause or roll back an agent.
Answer quality alerts
Output quality checked continuously against agreed evaluations.
AI-acceleratedService tiers
Customize the service model to the needs of each system
Not every system needs the same level of care. Tiers can be mixed across your portfolio.
| Feature | EssentialStable internal systems | AdvancedCustomer-facing and revenue systems | AutonomousCritical systems and AI agents |
|---|---|---|---|
| Incident and problem management | ✓ | ✓ | ✓ |
| Monitoring and alerting | ✓ | ✓ | ✓ |
| Coverage hours | Business hours | Extended | 24x7 |
| Full-stack observability | – | ✓ | ✓ |
| Error budgets and reliability reviews | – | ✓ | ✓ |
| Self-healing runbooks | – | Selected | ✓ |
| AI agent monitoring | – | – | ✓ |
| Monthly improvement backlog | ✓ | ✓ | ✓ |
Service levels and response times are agreed per system during onboarding.
Want fewer tickets next quarter?
Share a month of incident data. We’ll show you which issues automation could have caught, and what that would free up.