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Overview

ViceWire uses a combination of automated evaluation checks and targeted human review to reduce unsupported or misleading outputs before inclusion in production datasets.

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What the evaluation process is designed to catch

The current evaluation process is primarily designed to reduce two classes of error:
  • misclassification or incomplete structure, where content is assigned an event type, level of significance, or metadata that is not adequately supported by the source
  • unsupported interpretation, where direction, impact, or metadata is inferred more strongly than the source supports

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How quality is evaluated

ViceWire applies automated review checks to assess whether outputs remain consistent with the evidence supported by the source material and the product schema. Outputs that satisfy these checks can proceed to production. Outputs that do not may be held back and escalated for additional review. Review outcomes are used to improve decision rules and overall system performance over time.

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Human review

Given the scale of information from publicly accessible sources, not every output is manually reviewed. Human review is focused on exceptions, quality escalations, and cases where automated checks indicate elevated uncertainty or possible overreach.

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Current limitations

ViceWire does not currently publish public benchmark or third-party validation studies for output quality. The product should be evaluated through documentation review, live testing, and client diligence.