Platform monitoring
Monitor pipelines, refreshes, integrations, models, and AI workflows so failures are found before they disrupt the business.
Managed Data & AI
Keep critical systemshealthy and improving.
Ongoing monitoring, maintenance, and optimization for the data platforms, analytics, and AI solutions your business depends on.
Beyond go-live
Delivery is only the beginning.
Data and AI systems live inside changing businesses. Source systems evolve, schemas move, user needs grow, and models need attention. Without clear ownership, small issues quietly become unreliable reports and broken processes.
We provide the operational discipline and specialist capability to keep those systems reliable—while building a practical improvement cycle around them.
What we manage
One accountable partner after launch
Maintenance & support
Resolve incidents, manage routine updates, and keep data products aligned with changing systems and requirements.
Quality & governance
Track data quality, permissions, lineage, AI behavior, and operational controls across the managed environment.
Continuous optimization
Improve performance, reliability, cloud cost, user experience, and automation outcomes over time.
Supported technologies
Coverage across the data-to-AI stack
The managed scope is tailored to the platforms already powering your business.
Data platforms
Microsoft FabricAzure Data FactorySQL ServerDatabricks
Analytics
Power BISemantic ModelsDAXData Quality
AI & automation
Azure OpenAIOpenAIPower Automaten8n
Operations
Azure MonitorGitHubDockerREST APIs
How the service works
A visible, disciplined operating model
Support is structured around prevention, fast response, and steady improvement—not an undefined pool of hours.
- 1
Assess
Review the current environment, dependencies, risks, service history, and business priorities.
- 2
Transition
Document ownership, establish access, baseline performance, and create a controlled support handover.
- 3
Monitor
Track system health, refreshes, quality indicators, usage, cost, and AI behavior.
- 4
Respond
Investigate incidents, communicate impact, restore service, and document the resolution.
- 5
Improve
Prioritize recurring issues, performance opportunities, and useful enhancements.
- 6
Report
Share service health, work completed, risks, recommendations, and the next improvement plan.
Where it fits
Built for systems that cannot be left unattended
Managed support is useful when reliability matters but continuous specialist ownership is difficult to maintain internally.
- ✓Data pipelines that need dependable daily operation
- ✓Executive dashboards with business-critical refresh schedules
- ✓AI assistants requiring knowledge and prompt maintenance
- ✓Automations spanning email, documents, APIs, and business systems
- ✓Cloud environments needing performance and cost optimization
- ✓Teams that need specialist capability without a full in-house function
Governed operations
Clear control, ownership, and visibility
✓
Defined ownership
Clear responsibilities, escalation paths, and service boundaries keep support accountable.
✓
Secure access
Least-privilege access and controlled credentials protect the systems placed under management.
✓
Change control
Updates are assessed, tested, documented, and released through an agreed process.
✓
Operational visibility
Health reporting, incident history, and improvement tracking make service performance visible.
What you receive
A service you can see and measure
The engagement creates a clear operating rhythm around reliability, support, and continuous improvement.
- ✓Documented service scope and operating model
- ✓Monitoring, alerting, and incident response setup
- ✓Routine platform, dashboard, model, and workflow maintenance
- ✓Data quality and AI performance checks
- ✓Monthly service health and improvement reporting
- ✓A prioritized roadmap for continuous optimization
Frequently asked questions
A managed service with clear boundaries.
Scope, ownership, response, reporting, and improvement priorities are agreed and visible.
What can be included in a managed data and AI service?+
The scope can cover pipelines, integrations, cloud data platforms, dashboards, semantic models, AI assistants, automations, monitoring, incidents, routine maintenance, and prioritized improvements.
Can you take over an environment built by another team?+
Yes. We begin with a controlled transition that documents the architecture, dependencies, access, current risks, operating history, service expectations, and ownership boundaries.
How are incidents and changes handled?+
Responsibilities, severity, communication, escalation, testing, approval, release, and documentation are agreed in the operating model before ongoing support begins.
Will we receive visibility into service performance?+
Yes. Reporting can cover system health, incidents, data quality, usage, cost, work completed, recurring risks, and the next improvement priorities.
Need reliable ongoing ownership?