AI Observability Engineers Who Ship Production Monitoring
Your AI is a black box until you instrument it. We build production observability with LangFuse: full trace visibility, cost tracking, quality evaluation, and the dashboards your team needs to ship AI with confidence.
Technology Partners
Recognized by Clutch
What We Build with LangFuse
From tracing setup to full observability platforms, we deliver LangFuse solutions that give you visibility into your AI systems.
Full-Stack LLM Tracing
Instrument every LLM call, retrieval operation, tool invocation, and agent step with LangFuse traces. We give you complete visibility into what your AI is doing, how long it takes, what it costs, and where it fails. No more debugging production AI by reading logs.
Evaluation Pipelines
Automated quality evaluation that runs before every deployment. We build golden dataset testing, LLM-as-judge scoring, retrieval quality metrics (precision, recall, MRR), and regression detection so you catch quality drops before users do.
Prompt Management & Versioning
Use LangFuse prompt management to version, test, and deploy prompts without code changes. We set up A/B testing frameworks that compare prompt variants in production and automatically surface the winners based on your quality metrics.
Cost Tracking & Optimization
Real-time cost per request tracking, token usage analytics, and spending alerts. We build dashboards that show exactly where your AI budget goes and implement optimization strategies (caching, model routing, prompt compression) that typically cut costs 40-60%.
Quality Monitoring & Alerting
Continuous monitoring of AI output quality with automated scoring and alerting. We set up dashboards for latency percentiles, error rates, user satisfaction signals, and retrieval relevance, with PagerDuty and Slack integration for anomaly detection.
Dataset Management & Testing
Build and maintain evaluation datasets directly in LangFuse. We create annotation workflows for human labelers, manage golden datasets that grow over time, and automate regression testing against these datasets on every deployment.
Why AI Observability Requires Senior Engineering
Most AI teams ship models to production and hope for the best. When something goes wrong, and it always does, they dig through application logs trying to reconstruct what the LLM received and what it returned. This is not observability. This is archaeology. Production AI systems need the same level of monitoring that backend services have had for a decade: traces, metrics, alerts, and dashboards.
LangFuse is the open-source standard for AI observability, but installing it is not the same as using it well. The difference between a basic integration and production-grade observability is in the details: how you structure traces for multi-agent systems, how you build evaluation datasets that actually represent your users, how you set alert thresholds that catch real problems without creating noise, and how you use the data to systematically improve your AI system over time.
We have instrumented AI systems serving thousands of users daily. We know which metrics matter, how to build evaluation pipelines that scale, and how to turn observability data into actionable improvements. When you hire our team, you get engineers who treat AI monitoring as a first-class engineering discipline, not an afterthought.
Our Tech Stack
We work across the AI observability ecosystem and integrate with the tools your team already uses.
LangFuse Projects We Have Delivered
Real results from production AI observability deployments.
AI Sales Assistant Observability
Implemented full LangFuse tracing for a RAG-based sales assistant. Identified retrieval quality issues through evaluation pipelines, optimized prompts using A/B testing, and reduced LLM costs by 45% through model routing.
Read Case StudyMulti-Agent System Monitoring
Built comprehensive observability for a multi-agent system. LangFuse traces across all agent interactions enabled rapid debugging and quality optimization across coordinated workflows.
Read Case StudyChatbot Quality Monitoring
Deployed LangFuse monitoring for an enterprise chatbot. Real-time quality scoring, cost tracking per conversation, and automated alerts for quality regressions.
Read Case StudyHow We Work
A straightforward process from first call to production deployment.
Discovery Call
We start with a 30-minute technical conversation to understand your data, your users, and your constraints. No sales pitch. We dig into what you have tried, what failed, and what success looks like.
Architecture Proposal
Within a week, we deliver a detailed technical proposal: system architecture, technology choices with rationale, estimated timeline, and cost breakdown. You will know exactly what we plan to build and why.
Build & Ship
We build iteratively with weekly demos. You see working software from week one, not slide decks. Every PR is reviewed, every decision is documented, and we transfer knowledge continuously so your team can maintain what we build.
Frequently Asked Questions
Ready to See Inside Your AI Systems?
Tell us about your AI observability needs and we will respond within 24 hours with an initial assessment. Whether you need LangFuse setup, evaluation pipelines, or cost optimization.
Get a Free Assessment
Describe your AI observability needs and we'll assess how LangFuse can give you production visibility.