Observable
Can you detect problems before they propagate into decisions?
The Shift
Traditional observability catches pipeline failures. AI systems fail differently. A RAG pipeline can run perfectly while retrieving irrelevant chunks. An embedding model can drift silently over months. A model can hallucinate confidently with no error thrown. By the time you notice, thousands of flawed decisions have shipped. AI observability must monitor quality, not just execution.
Requirements
What must be true about the data itself.
- Data quality validated before AI consumption
- Retrieval quality monitored continuously
- Model outputs traceable to input data and retrieved context
- Drift detected across embeddings, schemas, and distributions
- Hallucination and faithfulness tracked in production
Capabilities
What your infrastructure must support.
Data quality testing
Validate completeness, correctness, validity before AI consumption
Retrieval quality monitoring
Track precision, recall, and relevance of retrieved chunks
Faithfulness/groundedness scoring
Detect when outputs diverge from provided context
Embedding drift detection
Identify silent degradation in vector quality over time
Hallucination detection
Flag outputs that fabricate or contradict source data
Input-context-output tracing
Link every decision to the data version and retrieved context that informed it
Anomaly detection
Identify unexpected values, volume shifts, distribution changes
Pipeline observability
Monitor execution, latency, and failures across DAGs