Document intelligence and compliance checking

DICE — Document Intelligent Compliance Engine

Turn complex document packs into structured, reviewable checks. AI extracts information; configured rules assess it; people review findings and corrections.

Public product
Visit product siteDiscuss your use case

DICE · Architecture

Exploded view of a layered document processing system

Architecture overview. Loading interactive sequence…

Scroll to explore the layers. This illustration does not establish DICE’s actual technical implementation.

Overview

How DICE checks a document pack

  • System
  • Input
  • AI step
  • Rule / logic
  • Human
  • Output
  1. 01System
    Configure

    An administrator defines required documents, fields and tolerances

  2. 02Input
    Upload

    PDFs or scans — potentially several documents in one pack

  3. 03AI step
    Extract

    AI reads and extracts relevant information

  4. 04Rule / logic
    Check

    Fixed rules compare values with the checklist and with other documents

  5. 05Human
    Review

    Inspect pass/fail findings, correct extraction, justify or escalate

  6. 06Output
    Report

    PDF report; report delivery by email is also described

Workflow as publicly described. AI interprets messy inputs, fixed rules produce the verdict and people review the finding.
01

The operational problem

An application or transaction may fail because supporting documents are missing, inconsistent or do not satisfy the receiving organisation's checklist. Reviewing a pack by hand can require repetitive reading, comparison and follow-up.

02

How the workflow operates

An administrator defines required documents, fields and tolerances. Users upload PDFs or scans, potentially with multiple documents in a pack. AI reads and extracts relevant information. Fixed rules compare extracted values with the configured checklist and with values in other documents. A reviewer can inspect pass/fail findings, expected and observed values, correct extraction mistakes, justify or escalate issues and produce a PDF report. The public description also includes report delivery by email.

03

What makes the architecture meaningful

The distinction between interpretation and decision logic is central. AI helps read messy inputs, while identifiable rule checks produce the verdict. A human remains able to review extracted values and the resulting finding. This is not a claim that an AI model has legal or regulatory authority.

04

Languages, deployment and boundaries

The product describes Arabic and English document reading, configurable checks, roles, separate departmental or customer spaces, audit records and local-model processing on customer-managed infrastructure. Banking and trade-finance review is described as available; insurance, government/licensing, visas, customs/logistics and onboarding configurations are on request. Specific OCR models, rule-engine implementation and performance figures have not been verified.

Evidence and status

Public product

Publicly described product; implementation not independently audited

A publicly described product.

Source: ice.tootms.com/welcome

Talk to us about the problem.

Start with the workflow, your constraints and what a successful implementation should achieve.

Get in touchExplore products