AI SRE tools fail not because of weak models, but because they lack proper observability foundations. Legacy systems with short retention windows, dropped high-cardinality data, and slow queries prevent AI from performing effective root cause analysis. ClickHouse's columnar architecture enables long-retention, high-cardinality observability at scale with sub-second query speeds, making it ideal for AI SRE copilots. The article presents a reference architecture combining ClickHouse with context layers (deployments, topology, incident history) and LLMs via SQL to create an investigative copilot that correlates data and surfaces insights while keeping humans in control of remediation decisions.