Stop AI context loss in production — observability, retrieval checks, tracing

Stop AI context loss in production — observability, retrieval checks, tracing targets a $24.0B = 200,000 engineering orgs x $120K ARR (AI reliability & observability spend across enterprise + mid-market) total addressable market with medium saturation and a year-over-year growth rate of 35%+ (AI/ML observability and MLOps spending growth as enterprises adopt LLMs).

Key trends driving demand: LLM adoption surge -- more production LLMs increase need for observability of context and retrieval; Vector DB maturation -- enables lightweight instrumentation of retrieval flows and faster dev integration; Shift to RAG architectures -- retrieval issues create a new class of production failures developers must detect; Observability consolidating to AI-aware platforms -- existing APMs and ML monitoring are integrating model context features.