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AI & Intelligent Automation

Flow Automation

AQBEE designs and implements Flow-based automation strategies that eliminate manual work, enforce business logic, and orchestrate processes across Salesforce clouds and external systems. From record-triggered automations and approval processes to screen flows and Flow Orchestrator multi-step workflows, we build maintainable, scalable automation architectures.

Capabilities

What We Deliver

Record-Triggered Flows

Automate actions on record creation, update, or deletion with before-save and after-save flows that enforce data quality, cascade updates, and trigger downstream processes.

Screen Flows & Guided Experiences

Build interactive, step-by-step guided experiences for complex data entry, onboarding wizards, and multi-object creation workflows with dynamic branching.

Flow Orchestrator

Design long-running, multi-step business processes with parallel branches, human approval steps, and background automation stages that span days or weeks.

Platform Event & External Integration

Trigger flows from platform events, external system webhooks, and scheduled jobs, enabling event-driven automation and cross-system process orchestration.

Error Handling & Governance

Implement fault paths, custom error handling, and logging patterns, with automation governance frameworks for naming conventions, documentation, and testing.

Real-World Applications

AI-Driven Use Cases

1

Automated Underwriting Workflow for Insurance

An insurance company uses Flow Orchestrator to manage the multi-step underwriting process — application intake, risk assessment, medical exam scheduling, approval routing, and policy issuance — reducing cycle time from 15 days to 4.

AI Component

Einstein AI performs initial risk scoring within the flow, auto-approving low-risk applications and routing high-risk cases to senior underwriters with pre-populated analysis.

2

Employee Onboarding Orchestration

An enterprise automates the new-hire onboarding process with Flow Orchestrator — triggering IT provisioning, HR documentation, manager tasks, training assignments, and 30/60/90-day check-in scheduling.

AI Component

Einstein GPT generates personalised onboarding welcome messages and training recommendations based on role, department, and location.

3

Order-to-Cash Automation for Manufacturing

A manufacturer automates the order-to-cash process with record-triggered flows — from CPQ quote approval to order creation, ERP sync via MuleSoft, shipment tracking, and automated invoicing.

AI Component

Einstein Prediction Builder flags orders with high probability of returns or payment issues, triggering additional review steps in the flow.

Case Studies

Flow Automation Success Stories

See how we've delivered measurable results with Flow Automation.

Intelligent Claims Automation for Property & Casualty Insurance
62% reduction
Claims Cycle Time
Insurance

Intelligent Claims Automation for Property & Casualty Insurance

Guardian Shield Insurance

Guardian Shield processed 180,000 property and casualty claims annually with an average cycle time of 23 days. The claims workflow was heavily manual — adjusters spent significant time on data entry, document review, and status updates. Customer satisfaction scores were declining as policyholders expected real-time transparency, and the company was losing market share to insurtechs offering faster, digital-first claims experiences.

Service CloudFlow OrchestratorEinstein AIAgentforce
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Predictive Field Service for Industrial Equipment Manufacturing
67% reduction
Unplanned Downtime
Manufacturing

Predictive Field Service for Industrial Equipment Manufacturing

Hamill Industrial Technologies

Hamill manufactured and serviced industrial compressors and HVAC systems across 3,000+ commercial sites. Their field service operation relied on reactive maintenance — technicians were dispatched only after equipment failures, resulting in 40+ hours of average customer downtime per incident. Scheduling was manual, parts inventory was managed in standalone systems, and technicians lacked mobile access to service history, manuals, and parts availability.

Service CloudField Service LightningData CloudEinstein Discovery
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Fleet Management and AI Route Optimisation for a National Delivery Network
18% → 6%
Deadhead Miles
Logistics

Fleet Management and AI Route Optimisation for a National Delivery Network

SwiftHaul Delivery Services

SwiftHaul operated a fleet of 1,200 vehicles making 35,000 deliveries per day across 28 metropolitan areas. Route planning was done manually by regional dispatchers using static zone assignments, resulting in 18% deadhead miles and frequent missed delivery windows. Drivers lacked real-time communication tools, vehicle maintenance was purely reactive, and the company had no visibility into fleet-wide fuel consumption patterns. Rising fuel costs and customer demands for precise delivery windows were squeezing margins below profitability thresholds.

Salesforce Field ServiceAmazon SageMakerAWS IoT CoreMuleSoft
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