Industry Supply chain & risk

The bottleneck in review work is not judgement. It is the dozen manual steps needed before any judgement can be made.

-57%Review headcount required
58%Of cases cleared within 10 minutes
38 hrs → 17 minPeak backlog clearance time
+15%Volume growth, with no overtime

Figures reflect relative change before and after implementation, presented in de-identified form under client confidentiality terms.

Close view of a document scanner feeder, sheets resting edge-on in the tray
AI-generated illustration

The bottleneck in review work is almost never judgement — it is getting hold of the information. Orders arrive through many channels, and alongside standard system integrations there is a long tail of web forms, emailed PDFs and photographed documents. Each one takes a person a dozen or more manual steps just to assemble the fields a decision needs.

Day to day this looks merely slow. It changes character the moment a checkout surge arrives: congestion becomes lost orders outright, adding people is the only short-term lever, and cost is therefore locked to volume.

We replace reviewing everything with managing exceptions. Multimodal extraction turns non-standard documents into structured data, a risk model handles triage, and people take only the genuinely marginal cases. The aim is not to replace reviewers but to move them off data entry and into risk analysis.

Case 01Supply chain · Risk

Order-source review: from reviewing everything to managing exceptions

The review process was heavily manual, the inputs were fragmented, and peak-period backlogs turned directly into lost orders. The fix was not more reviewers.

-57%Review headcount required
58%Of cases cleared within 10 minutes
38 hrs → 17 minPeak backlog clearance time
+15%Volume growth, with no overtime

The problem

KYC review of order sources was almost entirely manual.

Expensive and inelastic
Each case took 10 to 15 manual steps and 40 to 120 minutes.
Fragmented inputs
Alongside standard system integrations, orders arrived as web forms and as PDFs or images attached to email.
A capacity ceiling that cost orders
During order surges, volume spiked, review backed up, the team worked late, and waiting time on queued cases stretched far enough to lose business.

What we built

  1. 01
    A dual-track extraction architecturethat moves the operating model from "review everything" to exception management. Standardised sources such as API and EDI flow straight into an automated validation pool. Non-standard sources go through RPA and automated extraction, with multimodal AI and LLMs parsing PDFs and images to pull out registration numbers, responsible parties, line items and addresses.
  2. 02
    Risk scoring and automatic triageExternal digital-footprint checks combine with an internal transaction-behaviour model to score anomalies across order origin, trading history, delivery and contact details. Orders are routed into three tiers: cleared automatically, escalated for human review, or blocked. The system disposes of the large majority; only genuinely ambiguous edge cases reach a person.

The business case

The reviewers who stayed no longer do mechanical data entry. They work as risk analysts on the ambiguous middle tier, where judgement actually pays.

A leaner team runs the whole review line most of the time, with flexible reinforcement only at genuine peaks. The role moved up, rather than simply being cut.

The point of automation was never to replace reviewers. It was to give them time to actually review.

Need Professional Assistance?

Our team are senior engineers and former executives with 20+ years in commercial product development — from apps serving hundreds of millions of users to B2B SaaS platforms serving tens of thousands of merchants. In recent years the focus has been enterprise AI adoption, from mapping the opportunity through to tuning what is live. Whether that means:

  • Mapping AI opportunities and sequencing them
  • Preparing data flows and integrating existing systems
  • Building forecasting and optimisation models
  • Monitoring and tuning after go-live
  • Test strategy and process review
  • Building and adopting test automation

We can provide professional advice and concrete solutions for teams of different scales and types. If you're facing similar challenges, feel free tocontact our consulting team.