Collect
Ingest only the sources, time range and location agreed before collection.
Move from source evidence to engineer-confirmed findings.
The pipeline normalises and correlates events across agreed sources, then presents cited candidates for engineer review; it does not autonomously determine root cause.
01
Evidence pipeline
Events are normalised to a common timezone and timestamp format while preserving source-clock metadata and detected clock skew.
Classify
Parse source lines into versioned event schemas without discarding original evidence.
Correlate
Align related events across applications, clients, gateways and infrastructure sources.
Review
Present candidates and cited evidence to an engineer, who confirms or rejects each finding.
Events are normalised to a common timezone and timestamp format while preserving source-clock metadata and detected clock skew.
02
Potential sources
The minimum necessary source set is agreed before collection.
- Application and infrastructure journals, client logs and change records
- Crash dumps, Windows fault events, Bridge/Gateway and service output
- Host, container, cluster and network events
03
Feasibility sample
A representative sample is often sufficient for an initial feasibility review. We confirm the required time range and sources before collection.
A representative sample is often sufficient for an initial feasibility review; the required time range and sources are confirmed before collection.
04
Evaluation with the right evidence
When a labelled evaluation set exists, precision and recall can be measured alongside false-positive review, evidence coverage, reviewer disagreement and version records. Without a labelled set, we report evidence coverage and reviewer findings without claiming precision or recall.
Precision and recall are claimed only when a labelled evaluation set exists; otherwise the report uses evidence coverage and reviewer findings.
05
Deployment follows the client's data policy
Deployment location and model or provider are selected against the client's data policy and operated through an agreed change process.
06
Operational boundaries
AI assists evidence review; it does not operate the production system.
- AI proposes or summarises evidence; an engineer confirms findings.
- No trading signals, strategy design or trading-logic design.
- No autonomous production change or autonomous root-cause determination.
- Sources, retention, deployment location and model/provider are agreed before collection.
AI proposes or summarises evidence; an engineer confirms findings.
AI analysis provides no trading signals or strategy design and makes no autonomous production change or root-cause determination.