Your AI observability stack, configured and handed to your team.
We instrument your stack with LangFuse, LangSmith, or Helicone — dashboards, quality scoring, and alert thresholds configured against your baseline — then hand the keys to your engineering team. You own the infrastructure from day one.
3–5
Day Setup Engagement
Full
LLM Call Coverage
Per-step
Agent Tracing
Yours
Infrastructure Ownership
Why this matters
Most teams treat their AI stack as a black box. They know when users complain, not when quality drops. Setting up distributed tracing across LLM calls, tool invocations, and retrieval steps requires expertise in both AI observability tooling and your specific stack — and most engineering teams don't have time to do it right.
How We Do It
A structured process, every engagement.
Stack audit and instrumentation plan
We audit your AI stack — models, RAG pipelines, tools, orchestration — and select the right observability tool for your setup (LangFuse, LangSmith, Helicone, or Phoenix).
Configure instrumentation
We wire up distributed tracing for every LLM call and agent step. Your team's credentials, your infrastructure — we configure it, not host it.
Establish quality baselines
Baselines established for latency, output quality score, retrieval relevance, and tool use accuracy per endpoint — documented for your team's reference.
Set up dashboards and alerts
Alert thresholds and dashboards configured for quality drops, latency spikes, and retrieval failures — all inside your observability tool account.
Documentation and handover
Full runbook delivered: how to read the dashboards, what each alert means, and how to tune thresholds as your usage evolves. Your team owns it from here.
What You Get
Tangible deliverables, not slide decks.
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.
Teams without observability expertise
Engineering teams who know they need LangFuse or LangSmith but don't have bandwidth to configure it properly from scratch.
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 a Free Scoping Call →