LLM Observability
Traditional monitoring breaks down when applied to LLMs. Prompts, completions, token costs, hallucinations, and agent workflows need to be observed together, not in isolation. We extend your existing observability stack to cover AI workloads end-to-end.
Trace LLM and Agent Workflows
End-to-end traces across LLM calls, tool invocations, and multi-step agent workflows using OpenTelemetry-compatible instrumentation. Every prompt, retrieval, and completion captured at the span level.
Token Usage and Cost Monitoring
Dashboards and alerts for token consumption, model costs, and cost attribution by team or application. Identify expensive workflows and optimise prompt design before costs compound.
Hallucination Detection and Quality Monitoring
Automated evaluation frameworks that run quality checks on production traffic, flagging hallucinations, relevance drift, and response degradation before they reach end users.
RAG Pipeline Observability
Visibility into the full RAG pipeline retrieval quality, embedding latency, context window utilisation, and reranking effectiveness. Not just the LLM call at the end of it.
Multi-Agent Workflow Tracing
Most real AI failures emerge across turns, not within a single call. We instrument multi-agent systems to trace the full execution path across tool calls, memory lookups, and handoffs between agents.
Unified Stack Integration
LLM traces and metrics exported into your existing Prometheus and Grafana pipelines, unified view of AI and infrastructure health without a separate observability silo for AI workloads.
