Build blueprint
Data Pipeline Quarantine Router
A stream processor choosing accept, normalize, quarantine, retry, or human review per record.
- Problem
- Malformed or semantically odd records either fail whole jobs or silently contaminate downstream data.
- Why Jev
- Large pipelines need a constrained decision on many ambiguous records.
- Architecture
- Schema result + record sample + lineage → action Choice/confidence → deterministic transform queues.
- Current alternative
- Validation rules and catch-all dead-letter queues.
- Jev advantage
- Adds semantic triage without generating transformed data.
- Unknowns
- Throughput and handling sensitive data in model inputs.
Why this confidence: Bounded action space and measurable quality, with scale still uncertain.
This is an authored hypothesis derived from the research corpus. Nothing here demonstrates product demand, or that Jev performs well on this particular workload. Run the validation experiment before building past the MVP.
Sources
Limitations
- Throughput and handling sensitive data in model inputs.
- This is a research hypothesis, not evidence of product demand or Jev performance in this workflow.
Supporting research
- Cascade router PlausibleA cheap decision chooses whether to use rules, a small model, a premium model, a specialist, or a human.
- HA-Jev ObservedA Home Assistant custom integration exposing Jev evaluations as sensors and automation actions.
- Cheap decision calls could make tens or hundreds of semantic judgments per event economical. SpeculativeThe product design follows from published pricing and parallel question primitives, not independent production evidence.
- Parallel decision matrix PlausibleOne state is evaluated against many named questions, replacing repeated prompt/parse cycles.