Managed AI & Cloud Operations

AI platforms are living systems: models change, traffic grows, infrastructure ages, and cost profiles drift. Operating them well requires a discipline that most organizations are still building. Novera Intelligence provides managed operations for AI platforms — the monitoring, incident response, lifecycle management and capacity planning that keep AI dependable.

Our operations teams work to your service levels, whether we operate the platform entirely or run alongside your internal team with clearly defined responsibilities.

The Challenges We Solve

• No operational ownership

  AI systems built in projects often lack an operations home. We provide accountable operations with defined service levels and escalation paths.

• Alert fatigue and blind spots

  Generic monitoring misses model-specific failure modes. We implement AI-aware observability across infrastructure, serving quality and application behavior.

• Capacity surprises

  Demand growth and model changes create capacity crises. We forecast, plan headroom and manage upgrades as routine operations.

• Upgrade anxiety

  Model runtimes, drivers and platform components evolve constantly. We manage upgrades with staging, evaluation and rollback discipline.

What We Deliver

• AI platform monitoring

  Full-stack observability across infrastructure, GPU health, serving latency, model quality and application metrics.

• GPU cluster operations

  Day-to-day cluster administration: scheduling, tenant support, driver and runtime management, and health maintenance.

• Cloud administration

  Management of cloud environments: access, configuration, cost hygiene and compliance with your standards.

• Incident management

  Detection, triage, resolution and post-incident review with documented runbooks and clear escalation.

• Model lifecycle support

  Model versioning, deployment support, rollback management and retirement as models are superseded.

• Platform upgrades

  Staged upgrades of serving stacks, orchestration and infrastructure with evaluation gates and rollback paths.

• Capacity planning

  Forecasting and headroom management based on measured growth, with procurement timelines aligned ahead of need.

• Availability management

  Redundancy design, failover testing and availability reporting against agreed targets.

• Backup & recovery

  Backup strategy for models, configurations and data dependencies, with recovery testing as routine practice.

• Cost reporting

  Regular cost reporting with attribution, trend analysis and optimization recommendations.

• SLA-based support

  Support tiers matched to your criticality requirements, with documented response and resolution targets.

• Performance optimization

  Continuous tuning cycles that raise efficiency as workloads and usage evolve.

Architecture in Layers

An operating model for AI

1. Observe

   Unified telemetry across infrastructure, serving, models and cost.

2. Operate

   Runbook-driven administration of clusters, platforms and services.

3. Respond

   Incident management with defined severities, escalation and review.

4. Evolve

   Upgrade, capacity and lifecycle management as a continuous practice.

5. Report

   Service, availability and cost reporting with improvement actions.

How We Engage

Delivered through a disciplined lifecycle that carries the initiative from assessment to dependable, optimized operation.

1. 01

   Assessment

   Review of current estate, operational gaps, criticality tiers and support requirements.

2. 02

   Onboarding

   Monitoring integration, runbook development, escalation design and knowledge transfer.

3. 03

   Steady state

   Routine operations, incident response and reporting to agreed service levels.

4. 04

   Continuous improvement

   Quarterly reviews covering performance, cost, capacity and platform evolution.

Deliverables You Can Expect

• Operations model and responsibility matrix
• Monitoring and observability configuration
• Runbooks and escalation documentation
• Service level reporting
• Capacity plan and cost reports

Use Cases

• Managed inference platform

  A production AI platform operated to SLAs with quality monitoring, capacity planning and cost reporting.

• Co-managed GPU estate

  Novera operating platform layers alongside the customer's team, with a clear responsibility split and joint reviews.

• Cloud operations uplift

  An existing cloud AI environment brought to operational standard: monitoring, runbooks, incident process and cost hygiene.