[ Automated ] pipelines do the typing, humans approve[ NS · inbox-document-automation ]

Inbox & Document Automation

AI reads the inbound flood of emails, PDFs and forms, and turns it into clean, reviewed data your team approves, not retypes.

Isometric illustration of inbox and document automation: incoming documents read and structured through a pipeline with a human-approval checkpoint.
[ 01 ] The problem

A large share of your operation runs through a shared inbox. Requests, instructions, claims, changes, and documents arrive as unstructured text and attachments, and a person has to read each one, understand it, and re-key it into a system. It's slow, monotonous, the single biggest source of operational drag, and where errors creep in. Off-the-shelf tools choke on the messiness: the real attachments, the forwarded threads, the half-completed forms, the formats that change every quarter.

[ 02 ] What we build
  • A pipeline that ingests inbound mail and documents and uses AI to classify and extract the fields that matter.
  • A human-in-the-loop review workspace showing the original alongside extracted data, with field-level confidence scores.
  • Low-confidence items flagged for attention; a person approves before anything is committed.
  • Output is a clean import file or structured record, never an auto-sent action.
  • Every approval, edit, and rejection is logged. The 'pipelines do the typing, humans approve' rung.
[ 03 ] What you get
  • An ingestion pipeline for your inbound channels (shared inbox, attachments, common document formats)
  • An AI extraction and classification layer tuned to your document types and fields
  • A human-in-the-loop review workspace with field-level confidence scores and flagging
  • A structured output (import file or record) ready for the downstream system
  • A full audit trail of every extraction, edit, approval, and rejection
  • A small fixed-fee extraction proof up front, so you see accuracy on your real documents before committing

Why it matters

  • The rung behind the headline numbers
  • Marshall Peters cut document-review time by 70% with reviewed AI extraction in an insolvency CRM
  • One client freed 900 minutes of operational time a day (≈£120,000/year) by moving inbound onto a reviewed pipeline
  • The team stops typing and starts approving