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Advanced AI Observability

Full-stack observability from infrastructure to AI. Every signal in one place. Every cost tied to the workload that caused it.

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Centralized Dashboard

Infrastructure to Application Correlation

Predictive Alerts and Incident Management

Blind Spots That Kill AI in Production
Advanced Observability turns raw signals into action; full coverage, incidents routed to the right team, and a workflow your teams can actually keep up with.

AI Observability Metrics

You can watch CPU and latency all day and still have no idea what your agents are doing, what they're costing you, or whether the output is any good. This layer adds that visibility using the metrics, logs, and traces you already collect.

Signal Fragmentation

Your metrics and traces live in separate tools, so when something slows down you're stuck flipping between tabs trying to line them up. Pull them together and a latency spike shows you whether it's the model or the GPU.

No Ownership Routing

An alert goes off and everyone assumes someone else is on it. Send each incident to the team that can fix it (platform, app, or the AI itself) so it doesn't die in a shared channel.

No Path to Excellence

Every incident gets handled from scratch because no one really owns observability. A Center of Excellence gives it an owner and a standard the whole org can work from.

Four teams. One source of truth.

Everyone who answers for AI in production gets a view built for their job; from a single model run to the monthly bill. Powered by Grafana, hosted in the Kingdom.

AI & ML Engineers
Full-Stack Observability Infrastructure to AI
Full Coverage
One map for your hardware and your code. Track signals from routers to apps. See how Kubernetes affects your database in one view.
Actionable Response
Alerts for service health. Stop the midnight noise from 1% spikes. Manage your on-call schedules and escalation chains in the same dashboard.
AI Observability
Track token spend, GPU thermals, LLM response times, and VectorDB performance. Every request is categorized by type. Every cost visible.
On-Prem AI Observability
GenAI Visibility. Inside Your Perimeter. Your AI workloads don't leave your environment. Your observability shouldn't either.
01 Model Request Visibility
02 Token and Request Categorization
03 Cost Monitoring
04 RAG and VectorDB Monitoring
Scoping to Value in Weeks
Scoping
Weeks 1-2
Map what you have. Find what's missing.
Instrumentation
Weeks 3-8
Collect telemetry signals. Instrument infrastructure and AI workloads in parallel.
Implementation
Weeks 9+
Go live with full-stack observability.
Handover Train your team to operate the stack. Full documentation and knowledge transfer included.

Stop Guessing. Start Seeing.

Tell us what you're running. We'll tell you what it takes.

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Not ready for a demo? Talk to Engineering

Frequently Asked Questions

First dashboards and alerts typically land within weeks of instrumentation. Full implementation depends on scope and access cycles.

Yes. We instrument on-prem, cloud, hybrid, and air-gapped workloads.

Yes. LLM response times, token costs, GPU utilization, and VectorDB performance. For on-prem and air-gapped deployments, coverage includes model request visibility, token categorization, cost monitoring, and RAG performance. If you run Bunyan or Rekaz, the AI observability layer integrates directly

Managed service is available by contract. Coverage depends on scope and delivery model.

One incident example. Current dashboard access. Ownership contacts. We scope from there.