All five workflows
Use case 04 · Back-office operations

Document intake & data extraction

Claims, bills of lading, KYC packs and forms get read and re-keyed by hand. Extract every field, validate it and review by exception.

Document-heavy teamsInsurance claimsBanking KYCLogisticsReal estateHR onboarding
The evidence

Why this workflow costs more than it should.

20–40%Reduction in customer onboarding costs from AI-led domain transformations in insurance (McKinsey)1
£60MSaved by Aviva in 2024 after transforming motor claims with 80+ AI models (reported via McKinsey)1
33.8%Projected annual growth of the intelligent document processing market (Grand View Research)2

The problem

Claims forms, bills of lading, contracts, ID documents, medical reports and applications arrive by email and upload. Teams read each one, re-key the fields into a core system and chase anything missing. This work is slow, error-prone and grows linearly with volume. Hiring is the only way these teams scale today.

Market demand

Grand View Research values intelligent document processing at USD 3.0B in 2025, reaching USD 29.7B by 20332. Banking, financial services and insurance lead spending, and healthcare is the fastest-growing segment2. The same engine serves every document-heavy workflow, so one build can be reused across many clients.

Integration footprint

  • Input: shared inbox, upload portal or storage bucket
  • Engine: OCR plus an LLM that extracts data against a field schema
  • Rules: validation against the client's business rules and reference data
  • Output: CRM, claims, TMS or core-system API, or CSV
  • Review queue: side-by-side view of the document and extracted fields
The workflow

Five steps, one human checkpoint.

The agent does the repetitive work. People decide the exceptions. Every action is logged against the baseline.

01 · RECEIVE

Split & classify

Separate multi-document packs and identify each document type.

02 · EXTRACT

Fields to schema

Pull every required field, with a confidence score and the source location.

03 · VALIDATE

Rules & completeness

Cross-check the fields and request anything missing from the sender automatically.

04 · HUMAN CHECK

Review by exception

Only low-confidence fields are routed to a reviewer. Straight-through rate rises over time.

05 · WRITE

Post & trace

Create the record in the core system, keeping a link to the original document.

KPIs tracked from day oneCost per documentStraight-through rateField accuracyTurnaround timeRework rate
The product

What your team works in.

One screen for the queue, the evidence and the decision. Exceptions come with the reason and a suggested action, so reviewing one takes seconds.

Product preview · sample data
The savings model

What it could be worth.

Market research model · based on published benchmarks

Intake team of 6 full-time staff

We assume a loaded cost of USD 70k per FTE; replace it with the client's own figure. The saving range applies McKinsey's reported 20–40% onboarding-cost reduction to the intake team's cost. Capacity freed is redeployed, not necessarily cut.

Baseline 6 FTE × $70k$420,000 / yr
Low case (−20%)$84,000 / yr
High case (−40%)$168,000 / yr
$84k–168k / yr
How to read this model. It uses the published benchmark as the baseline and states every assumption. Your own figure comes from measuring your workflow during the feasibility audit.

Sources

  1. 1
    McKinsey & Company, The future of AI for the insurance industry (Aviva case; 20–40% onboarding-cost reduction). mckinsey.com/industries/financial-services/our-insights/the-future-of-ai-in-the-insurance-industry
  2. 2
    Grand View Research, Intelligent Document Processing Market Report: USD 3.0B (2025) to USD 29.7B (2033), 33.8% CAGR 2026–2033. grandviewresearch.com/industry-analysis/intelligent-document-processing-market-report
Let’s talk

Which of these could run in your company?

A feasibility audit finds your best-fit solution, or book a call and walk through the cases that match your operation. No sales reps, ever.