evidence-builder

Extracting structured facts from raw logs and traces involves collecting multi-source observations and transforming them into auditable evidence objects with explicit timestamps, confidence scores, and provenance, ensuring data remains testable and replayable for incident reviews.

The best way to turn heterogeneous metrics and tickets into auditable evidence is to apply a fact-extraction layer that converts noisy cross-source observations into structured, scored, and comparable evidence packages without performing open-ended inference.

Yes, you can use structured evidence extraction for post-mortems without inferred assumptions by enforcing a non-inference principle. Evidence must be built prior to open reasoning, capturing only explicit facts with provenance and confidence scores attached to hypotheses.

Adding confidence scores and provenance to raw observability data requires structuring each extracted fact into an evidence object. Every item must explicitly record its source, timestamp, and a confidence score to remain auditable and comparable across different observation types.

Yes, evidence extraction works with code, config, and release notes for root cause analysis. It collects heterogeneous raw observations from these diverse sources to form structured evidence objects, extending its world view when validation demands more context.

A key limitation of building evidence objects from raw observations is the strict non-inference constraint; it only performs fact extraction and cannot generate open-ended reasoning. It requires explicit structure and provenance, reporting blind areas when context is insufficient.