LLM Tracing

Full visibility into every LLM call, trace, and output.

We instrument your AI stack to capture real-time tracing, latency, token usage, output quality scoring, and retrieval relevance — so you know exactly what your AI is doing and where it fails.

100%

LLM Call Coverage

Real-time

Quality Scoring

Per-step

Agent Tracing

Weekly

Observability Reports

Why this matters

Most teams treat their AI stack as a black box. They know when users complain, not when quality drops. Without distributed tracing across LLM calls, tool invocations, and retrieval steps, diagnosing performance issues or output quality regressions takes days — or never happens at all.

How We Do It

A structured process, every engagement.

01

Stack audit and instrumentation plan

We audit your AI stack — models, RAG pipelines, tools, orchestration — and define the tracing instrumentation points.

02

Instrument with distributed tracing

We integrate with LangSmith, Arize, Phoenix, or Weights & Biases depending on your stack, and instrument every LLM call and agent step.

03

Define quality baselines

Baselines established for latency, output quality score, retrieval relevance, and tool use accuracy per endpoint.

04

Configure alerts and anomaly detection

Alert thresholds set for quality drops, latency spikes, retrieval failures, and anomalous outputs.

05

Weekly observability reports

Delivered to your team: quality trends, anomaly summaries, and recommendations per observation period.

What You Get

Tangible deliverables, not slide decks.

Instrumented AI stack with distributed tracing
Quality score per LLM call and agent step
Latency, token, and retrieval relevance dashboards
Alert configuration for drift and anomalies
Baseline documentation per endpoint
Weekly observability quality reports

Who It's For

Built for teams where AI reliability is non-negotiable.

Production-blind teams

AI is deployed but you have no visibility into response quality, latency variance, or retrieval failures in real traffic.

Latency-sensitive applications

Customer-facing AI where slow or low-quality responses have a direct impact on user experience and retention.

Multi-model environments

Teams running multiple models or providers who need unified visibility and consistent quality measurement across all of them.

Ready to get started?

Book a free 30-minute AI Reliability Assessment. We'll review your stack, identify your highest-risk failure modes, and show you exactly what to fix first.

Book Your Free Assessment →