Pipeline Failure Audit
Lineage, retry behavior, mutation points, state boundaries, and recovery gaps across production data paths.
Data reliability systems for production AI
AI systems fail. We make them recoverable.
Versioned data paths, deterministic execution, and tested recovery controls for model-input pipelines in production.
controlinput lineage
controlstate checkpoints
controlreplay logs
Recovery control planerun: prod-8421
Failure model
Production failures usually start below the model boundary.
Uncontrolled path
- Mutable datasets with no rollback target
- Retries that produce different outputs
- Partial writes hidden inside downstream state
Controlled path
- Dataset versions bound to each run
- Replayable jobs with stable inputs
- Rollback pointers tested before incidents
Engineering scope
What we inspect, design, and operate.
Deterministic Data Architecture
Versioned datasets, input hashes, explicit lineage, and execution boundaries that reproduce under load.
Replay & Rollback Systems
Checkpoint stores, rollback pointers, replay workers, and isolated re-runs with observable state.
Reliability Operations
Change review, drift controls, recovery drills, runbooks, and incident-path validation.
Control plane
Cadence Recovery Layer
A reliability layer around model-input data paths. It records state, isolates failed runs, and provides a deterministic path back to a verified version.
Snapshot material state boundaries
Quarantine corrupted runs
Replay from immutable inputs
Emit recovery evidence
Cadence recovery layerdeterministic replay path
Runtime context
Designed around the failure modes of modern data infrastructure.
Kubernetes
job restarts, pod eviction, environment drift
Kafka
offset replay, ordering, poison messages
Airflow
backfills, retries, partial DAG state