Observability for the AI Infrastructure Era

Our Mission

RAG (Retrieval-Augmented Generation) has become the backbone of enterprise AI applications. But unlike traditional web applications with well-established observability tools, RAG pipelines operate in a blind spot — quality degrades silently, costs spiral undetected, and failures cascade before anyone notices.

Obsyn provides the missing observability layer: real-time quality scoring, cost attribution, and infrastructure monitoring purpose-built for the unique challenges of ML-powered search and generation systems.

Why RAG Needs Its Own Observability

• •Quality is non-deterministic — same query can return vastly different answers
• •Failures are subtle — degraded retrieval doesn't cause errors, just worse answers
• •Cost attribution is complex — embedding, retrieval, and generation costs must be tracked per query
• •Infrastructure is heterogeneous — vector stores, embedding models, LLMs all need unified monitoring
• •Optimization requires data — you can't improve what you can't measure