Vision

We're building an AI Data Reliability Engineer (DRE) that uses autonomous agents to assist in data incidence resolution with full context of your ML/data stack.

• Correlates metrics, logs and changes instantly. Zero context switch.
• Identifies root cause hypothesis with suggested fix
• Maps your data infrastructure in real-time via a versioned, run-level lineage graph

The "Data Context" Gap in Data Engineering

For years, data engineering followed a predictable, linear path. We built pipelines, defined a run schedule 0 * * * * and monitored health through dashboards with siloed context.

When a pipeline failed, a human paged through logs and dashboards to visually correlate a code change to a downstream spike in errors. It was a manual, contextless loop of debugging: one 2AM alert, one engineer and endless context switching between dashboards. Investigation moved at the speed of human intuition.

Beyond the Dashboard: The AI Era of Evidence-Based Investigation

As data infrastructure grows in complexity, the "data context" gap widens, fast. A human can't keep the entire data dependency graph in their head. At 2AM, the investigation becomes unmanageable as existing observability tools fail to close the root-cause loop.

This is what oleander will do. Oleander is defining the era of AI-native observability by providing an independent telemetry context layer for ML/data infrastructure, an interface where humans and agents can learn and debug together.