GoKawiilUnsiloed and TypeSafe's Jev combine to flag OCR errors in document extraction
A workflow pairs Unsiloed, which extracts structured fields from documents and locates them on the page, with TypeSafe's Jev, which evaluates extracted data against targeted questions. Jev separates input data (state) from the checks it runs (questions), returning typed answers such as probability scores, multiple-choice selections, or rubric-based ratings. The combined output is used to flag which extracted fields likely need human review, illustrated with a six-row medication list containing a stained dose and an implausible but clearly printed quantity.
GoKawiil's interpretation of the reporting above, not reported fact.
The approach addresses a subtle failure mode where OCR reads text correctly but the underlying value is still wrong, such as an extra zero in a dosage, which standard confidence scores alone may not catch. By layering a reasoning/verification step (Jev) on top of extraction (Unsiloed), teams could reduce silent errors in high-stakes documents like medical records before they propagate downstream. This suggests a broader pattern of pairing extraction tools with separate verification models rather than trusting extraction confidence scores alone.
- Unsiloed extracts structured fields and their locations from documents; Jev evaluates those fields against specific questions.
- Jev supports three answer types—Noul (probability), Choice (named options), and Score (rubric levels)—each returning different confidence signals.
- The combined pipeline aims to catch errors that survive good OCR, such as implausible values or obscured data, by routing uncertain fields for review.
Source: unsiloed.ai, 2026-10-13
Published there as: “Document Review with Unsiloed and TypeSafe Jev”
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