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Intelligent Workflow Automation

Automate the work around decisions, and keep people in charge of the decisions.

Automation has three tools that are often confused: deterministic workflow, robotic process automation and AI agents. Each fits a different kind of work. Choosing the wrong one creates fragile bots or unpredictable agents.

We start from the process, mine how it actually runs, and combine the right mix of workflow, integration, RPA and AI, with humans approving the steps that carry risk.

The problem

What usually gets in the way

  • High-volume processes depend on manual re-keying between systems.
  • RPA bots break whenever a screen changes.
  • Teams want AI agents but cannot explain how they will be controlled.
  • Nobody knows where the process actually loses time.

What we deliver

Capabilities

01

Process discovery

Process and task mining to see how work really flows and where time is lost.

02

Automation design

Choose workflow, API integration, RPA or agents step by step, by risk and variability.

03

Document AI

Extract and validate data from forms, invoices and correspondence, including Arabic.

04

Agents with guardrails

AI agents limited to approved tools, with human approval and audit for risky steps.

05

Integration

APIs and events between core systems instead of screen-scraping where possible.

06

Operations

Monitoring, exception handling and continuous improvement of live automations.

Delivered by

Services involved

The service lines combined to deliver this solution.

How it fits together

Reference architecture

A vendor-neutral outline. Components are selected for your workloads, skills and regulatory context.

  1. WorkCase and task inboxesApprovalsCustomer and employee portals
  2. AutomationWorkflow and BPM engineAI agents and document AIRPA for legacy screens
  3. IntegrationAPIs and eventsIntegration platformMaster data
  4. ControlAudit trailException handlingProcess analytics

How we engage

Start small, prove value, scale

  1. 01Assess

    3–4 weeks

    Mine priority processes and build the automation business case.

    • Process maps
    • Automation candidates
    • Business case

  2. 02Pilot

    6–10 weeks

    Automate one end-to-end process with measured results.

    • Live automation
    • Exception handling
    • Cycle time vs. baseline

  3. 03Scale

    Ongoing

    Build an automation pipeline and centre of excellence.

    • Automation portfolio
    • Reusable components
    • Benefits tracking

Outcomes

What we measure

  • Process cycle time
  • Straight-through processing rate
  • Manual effort removed
  • Error and rework rate
  • Exceptions handled within SLA

Where it applies

Example use cases

  • Banking

    Account opening and KYC document processing.

  • Government

    Permit and licence application handling.

  • Logistics

    Shipment documentation and exception management.

  • Healthcare

    Claims and pre-authorisation processing.

Accelerators

Reusable assets we are building

Delivered today as services. These accelerators are being developed from that work to shorten future engagements.

In development

Automation fit assessment

A scoring model to choose workflow, RPA or agent per process step.

In development

Document AI templates

Extraction and validation patterns for common bilingual documents.

Technology

Platforms we design around

Vendor-neutral: examples of platforms commonly used for this solution. Names identify technologies only and do not imply a partnership.

  • UiPath
  • Microsoft Power Platform
  • Celonis
  • Pega
  • Appian
  • Camunda
  • MuleSoft
  • Automation Anywhere
See the full technology ecosystem →

Insights

Further reading

Agents, Workflow or RPA?

Most processes need judgment at a few steps, not everywhere. Build the process spine as a deterministic workflow, use RPA only where there is no API, and give agents the narrow steps where judgment over messy input actually pays.

Read →

Agentic AI in the Enterprise

The hard part of enterprise agents is no longer building one. It is making many of them reliable, affordable and governable, on products that change faster than procurement cycles. That is a control-plane problem before it is a model problem.

Read →

Human-in-the-Loop Design

Adding a reviewer does not make automation safe. Human oversight works only where the human adds information the system lacks, and only if you measure whether reviewers actually catch errors.

Read →

Designing Agents With Guardrails

An instruction in a prompt is a request, not a control. Design every agent on the assumption that its model will eventually be manipulated, and put the guardrails where the model cannot reach them.

Read →

Talk to us about Intelligent Workflow Automation

Start with a short assessment: we will rank the opportunities, check readiness and agree how success will be measured.

Start a conversation

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