Skip to content

Post

CommunityJev Cookbooks

Document Review With Unsiloed and TypeSafe Jev

A walkthrough that pairs Unsiloed document extraction with Jev: extracted medication rows and their citations become Jev's state, one Noul question per row flags implausible or obscured doses, and the results decide which fields need review.
Format
Post
Reading time
3 min
Published by
Community
Author
Unsiloed AI
Last verified
Oct 2026

Highlights

  • Unsiloed extracts structured fields and locates them on the page; Jev then judges each extracted value against focused questions.
  • Each field carries extraction_score and grounding_score, plus a citation with page and bounding box on the original scan.
  • All six medication rows share one request; each question is evaluated independently, with row IDs matching answers back to rows.
  • A Noul of 0.04 is a 4% probability of yes — a strong no — not a statement that Jev is 4% confident.
  • Requests post to https://api.typesafe.ai/v1/systemone with model jev-1.13.0 and a TypeSafe API key in the Authorization header.

Quickstart

ts
const request = {
  model: "jev-1.13.0",
  state: {
    context: "Check for obvious quantity, unit or frequency errors. Dose means amount taken each time, not supply quantity. Do not assess patient-specific safety or propose corrections.",
    medications: [{ id: "row-5", medicine: "Cetirizine 10 mg tablet", dose: "1 tablet", frequency: "Once daily as needed" }]
  },
  questions: {
    "row-5": { type: "noul", instructions: { question: "Is the extracted dose and frequency plausible on its face for this medicine and strength in the stated adult outpatient context, without an obvious quantity or frequency transcription error?", row_id: "row-5" } }
  }
};

Watch out

Grounded in the truncated Unsiloed blog post read on 2026-10-15; the excerpt stops at the API description, so the routing code, costs and interactive prescription example are not covered here. All figures come from a single six-row medication example, and the post notes Jev judges the extracted text without seeing the image or Unsiloed's scores.

Questions about this entry

Short answers drawn from the linked project or page.

Reactions & coverage

Posts, threads, and videos about this entry from around the web.

X: Finding a signature on a scanned form

X: A data analyst agent with Jev as the review layer

X: A 12-page PDF on building a Jev harness

X: A review of Jev for AI workflows

From the Jev guides

Original write-ups on what this entry is for.

From the Jev AI model community

Posts from builders shipping with Jev right now.

Follow @typesafeai