Unmonitored AI workflows
Deployed AI agents and integrations run without regular review of behavior or cost.
AIOps
Production AI needs babysitting. Quality drifts, costs creep, edge cases appear.
AI in production isn't set-and-forget. Output quality drifts, token costs sneak up, and real usage surfaces edge cases the pilot never did. We keep production AI healthy: monitoring quality and cost, watching for drift and misuse, and tightening or loosening guardrails as trust and usage evolve. The kind of ongoing care most teams don't have the bandwidth for.
Architecture
TLS 1.3 / mTLS
NIST Aligned
Zero Public Egress
Monitor
Detect
Remediate
Improve
Platform health: 78%
Operational Burden
Managed AI Operations challenges we solve.
Managing 24/7 incident response, timely security patching, uncontrolled cloud spend, and complex multi-platform estates.
Unreviewed tool access
Agent and connector permissions are not periodically reassessed as usage grows.
No improvement process
AI workflows launch and then stay untouched, even as needs change.
Solution overview
Managed AI Operations designed for production readiness.
Output quality drifts, token costs sneak up, and real usage surfaces edge cases the pilot never did. We keep production AI healthy: monitoring quality and cost, watching for drift and misuse, and tightening or loosening guardrails as trust and usage evolve. The kind of ongoing care most teams don't have the bandwidth for.
AI workflow monitoring
Output quality, token cost and usage patterns tracked after launch, not just at go-live.
Access review
Agent and tool access reviewed periodically as usage and trust evolve.
Prompt and tool controls
Guardrails tightened or loosened based on real production behavior.
Improvement backlog
Ongoing care for the edge cases and drift that a pilot never surfaces.
Operating Architecture
Proactive monitoring, incident escalation & SLA operating model.
Continuous health telemetry, automated alert deduplication, disciplined runbooks, and executive reporting visibility.
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Understand the business goal, current environment, risks and constraints before any design work begins.
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Design the target operating model, controls and integration points so the solution fits how the business actually runs.
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Build and configure the solution using repeatable, reviewed engineering practices, not one-off manual work.
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Test against real scenarios and security requirements before anything reaches production.
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Hand over documentation, runbooks and support so the client team can run the result with confidence.
Delivery model
5 Phases
Milestone-Gated Delivery Model
Every phase ends with a documented deliverable and a clear go/no-go decision before the next one starts.
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Documented Deliverable & Formal Go/No-Go Decision Gate
No code or infrastructure moves to production without passing automated compliance checks and client review.
Zero Vendor Lock-In
Delivered into client Git & cloud accounts
Infrastructure-as-Code
Terraform / Bicep repeatable blueprints
Operational Runbooks
Knowledge transfer & team enablement
Managed Engagement Lifecycle
From estate onboarding and runbook review to continuous optimization.
Structured onboarding, baseline configuration hardening, 24/7 monitoring, monthly FinOps reviews, and regular posture reviews.
Assess current AI workflows
Review what agents, integrations and tools are already deployed.
Establish monitoring
Track behavior, cost and performance of AI workflows.
Review access regularly
Reassess agent and tool permissions on a cadence, not just at launch.
Maintain improvement backlog
Evolve workflows as needs and usage patterns change.
Peace of Mind & Agility
Guaranteed uptime, less burnout and predictable costs.
Free your core product team to innovate while certified engineers ensure uptime, security patching, and platform reliability.
Monitored AI operations
AI workflow behavior, cost and performance are reviewed on an ongoing basis.
Periodic access review
Agent and tool permissions are reassessed instead of left unchanged indefinitely.
Continuous improvement backlog
AI workflows evolve through a maintained backlog instead of standing still.
Operations Stack
Enterprise monitoring, ITSM, backup & telemetry toolchain.
Production-grade observability and automation platforms providing immediate alerting and reliable telemetry.
Azure OpenAI
AI Foundry
MCP
Monitoring
Audit logging
Managed Operations FAQ
Important questions on SLAs, escalation paths and onboarding.
Clear information regarding support boundaries, response times, and handover expectations.
Does AI really need ongoing management?
Yes: quality drift, cost creep and new edge cases are the norm, not the exception.
What do you monitor?
Output quality, token cost, guardrail effectiveness, and unusual or unsafe usage patterns.
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