Documents, triaged.

Professional-services teams receive forms, letters, and supporting documents in many formats. Skilled people spend time reading and re-typing instead of doing the work the documents are for.

This study designs an intake pipeline that classifies documents, extracts the fields that matter, validates them, and sends uncertain cases to a person with the source text alongside.

How can document intake be faster and more consistent without accepting extraction errors into systems of record?

Extraction will sometimes be wrong. The design has to make it obvious when, and make correction faster than starting from scratch.

• Varied inputs

  Scans, photos, PDFs, and email bodies, in varying quality.

• Sensitive content

  Documents can contain personal data; retention and access rules apply.

• Accountability

  Every value written to a system of record must be traceable to its source and reviewer.

• Spiky volume

  Deadlines create sudden peaks that the pipeline must queue rather than drop.

WHAT DISCOVERY WOULD ESTABLISH

1. 01

   Document sampling

   Collect representative examples of each document type, including poor-quality ones.

2. 02

   Field priorities

   Agree which fields matter for downstream work and what an error in each would cost.

3. 03

   Current review practice

   Understand how people check documents today and what they look for.

4. 04

   Labeled test set

   Create a reviewed set of documents with correct values to measure extraction.

Documents arrive through email, upload, or scanning, and are queued for OCR, classification, and structured extraction. Each field carries a confidence signal and a link to its location in the source.

Schema rules and cross-checks validate the result. Documents that pass go forward; anything uncertain goes to a review queue with the source and extracted values side by side.

CAPABILITIES

• Multi-channel intake
• Document classification
• Structured field extraction
• Validation rules and cross-checks
• Side-by-side review queue
• Field-level audit trail

1. DECISION 01

   Review by exception

   WHY

   People focus on uncertain documents instead of re-checking everything.

   TRADE-OFF

   Thresholds start conservative, so early review volume is higher.

2. DECISION 02

   Show the source next to every field

   WHY

   Reviewers verify a value in seconds when they can see where it came from.

   TRADE-OFF

   Requires storing positions for each extracted value.

3. DECISION 03

   Corrections improve the system

   WHY

   Every correction is stored and added to the evaluation set.

   TRADE-OFF

   Corrections need periodic review before they are used.

Each stage is a queued, retryable step. Model output is never written directly to a system of record; it passes validation and, where needed, human review first.

1. L1

   Intake

   Receives documents from every channel into one queue.

   • Email inbox
   • Upload portal
   • Scanner
2. L2

   Understanding

   Turns pages into text, types, and candidate field values.

   • OCR
   • Classification
   • Field extraction
3. L3

   Validation

   Checks values against rules, formats, and each other.

   • Schema rules
   • Confidence thresholds
   • Cross-checks
4. L4

   Review

   Where people confirm or correct uncertain results.

   • Review queue
   • Source side-by-side
   • Corrections
5. L5

   Systems of record

   Receives only validated or reviewed data.

   • Case system export
   • Audit log

DESIGN PRINCIPLES

1. Models propose, rules decide

   Deterministic validation sits between model output and any downstream write.

2. Traceable values

   Each field links to its page, position, model version, and reviewer.

3. Queue every stage

   Peaks are absorbed by queues, and failed steps retry without reprocessing everything.

PROPOSED DELIVERY SEQUENCE

1. PHASE 01

   One document type

   Start with the highest-volume type and its most important fields.

2. PHASE 02

   Measure on the test set

   Score extraction per field before connecting any downstream system.

3. PHASE 03

   Review-everything pilot

   Route all documents to review at first, to calibrate thresholds with real data.

4. PHASE 04

   Raise automation gradually

   Relax thresholds per field only where evidence supports it.

1. Extraction accuracy

   Field-level scoring against the labeled set on every model or prompt change.

2. Validation rules

   Unit tests for each rule and cross-check, including edge formats.

3. Poor-quality inputs

   Skewed, low-resolution, and multi-page scans in the regression set.

4. Review workflow

   End-to-end tests for confirm, correct, and reject paths.

1. Isolated processing

   Document storage and processing in a restricted environment with encryption at rest.

2. Retention controls

   Automatic deletion according to agreed retention periods.

3. Queue monitoring

   Backlog size, processing time, and review age tracked with alerts.

1. CHALLENGE 01

   Confident but wrong extractions.

   RESPONSE

   Cross-checks between related fields and conservative per-field thresholds.

2. CHALLENGE 02

   New document layouts.

   RESPONSE

   Unknown layouts are classified as such and routed to review instead of forced into a type.

3. CHALLENGE 03

   Reviewer fatigue.

   RESPONSE

   Highlight only uncertain fields, and keep the review screen fully keyboard-driven.

This is a concept, so there are no measured results. The outcomes below are what the design is intended to achieve; the measures after them are how a pilot would verify it against an agreed baseline.

INTENDED RESULTS · QUALITATIVE

• People review uncertain documents instead of re-typing every one.
• Every value in a system of record can be traced to its source and reviewer.
• Peaks are queued and processed rather than piling up in inboxes.

HOW RESULTS WOULD BE VERIFIED

• Field-level extraction accuracy on the labeled test set.
• Share of documents needing review, tracked as thresholds change.
• Handling time and downstream correction rates against the current baseline.

1. 01

   Plan for being wrong

   The review experience matters as much as extraction quality.

2. 02

   Per-field, not per-document

   Confidence and automation decisions work best at the field level.

3. 03

   Keep a labeled set alive

   Corrections feed a test set that protects against regressions.