The new perk · human-in-the-loop

99% accuracy still means 1 in 100 wrong.

The Review Inbox is a purpose-built queue for the fields the AI isn't sure about. Set a confidence threshold; anything below it lands here for a human to approve or correct in seconds.

INVOICE
#INV-2025-00482

Extract Metadata

2 flagged · 0/6 resolved

DOC #8829-X
99.8% conf.
97.1% conf.
64.2% conf.
58.6% conf.
98.1% conf.
99.9% conf.

Try it: approve clean fields, edit the flagged ones. Corrections become training signal for your account.

Connected · live queue

Documents submitted via intake

Anything sent through the intake channels lands here. Approve to release downstream, or request changes to bounce it back.

+ Submit a document
Reviewer identity

Who's reviewing today?

Your name and email are attached to every approval, change request, and field edit in the audit log.

Per-field confidence

Every extracted field carries a confidence score derived from token-level model probabilities, not a single doc-level guess.

Custom thresholds

Set the floor per workflow. Invoices might need 95%; lead-gen forms might be fine at 75%.

Assignees & SLAs

Route by document type or amount. Hit the SLA timer and we escalate to a backup reviewer.

Side-by-side review

Document on the left, fields on the right. Click a field to highlight its bounding box in the source.

Full audit log

Who approved what, when, with the original value preserved. Export to your compliance system.

Training feedback loop

Corrections are stored and (with consent) used to fine-tune a private model on your dedicated instance.

With vs. without the Review Inbox

Without
  • · Bad data silently enters your CRM
  • · Engineers babysit a spreadsheet of exceptions
  • · No audit trail of who changed what
  • · Errors only surface when a customer complains
  • · The model never learns from its mistakes
WITH PARSE.ME
With
  • ✓ Only verified data reaches downstream systems
  • ✓ Reviewers triage in a dedicated, fast UI
  • ✓ Immutable audit log of every approval
  • ✓ SLA timers and escalation built in
  • ✓ Corrections feed back into your private model

Close the loop on extraction.

Open my workspace