AI & Agentic AI Development

Arova Singapore

Client

Arova Singapore

Project Nature

Agentic AI Automation Platform

Services

Agentic AI Software Development

Overview

Project Overview

Arova receives customer enquiries and purchase orders through multiple channels, including email, WhatsApp and fax. Processing these requests manually requires employees to review each message, identify the relevant information and enter the order details into the company’s operational system. OTG Lab proposed an AI-powered order automation platform that centralises incoming requests, classifies their intent and extracts key information such as customer details, products, quantities and delivery requirements. The system then generates a draft sales order for staff review before validating customer credit and stock availability through AutoCount. It also assists employees in preparing suitable customer responses, creating a faster and more controlled order management process.

Challenges

Challenges We Solved

Arova’s order-processing workflow depended heavily on manual review and data entry across multiple communication channels. This created operational delays, inconsistent data handling and limited visibility into the status of enquiries and orders.

Fragmented Order Intake

Centralised enquiries and purchase orders received through email, WhatsApp and other supported channels.
Time-Consuming Data Entry

Used AI to identify and extract important order information from unstructured customer messages and documents.
Inconsistent Order Information

Standardised extracted data before presenting it to employees for verification and approval.
Limited Stock and Credit Visibility

Connected the review workflow with AutoCount to support customer credit and inventory validation.
Repetitive Customer Communication

Enabled AI-assisted response drafting while keeping employees in control of the final message.

How It Works

Our Approach

Our approach focused on combining AI automation with human oversight to improve Arova’s existing order management process without disrupting daily operations. We first mapped the current workflow across email, WhatsApp and other order channels, then designed a centralised platform capable of classifying enquiries, extracting order information and generating structured draft orders. Key validations, including customer credit and stock availability, were incorporated through AutoCount integration, while staff remained responsible for reviewing and approving all AI-generated outputs. This phased and practical approach helped ensure that the solution was accurate, scalable and aligned with Arova’s operational requirements.

Project Highlights

Workflow Discovery: Mapped Arova’s existing enquiry, quotation and order-processing workflows across different communication channels.
AI-Assisted Order Intake: Designed an AI layer to classify incoming messages and extract structured information from emails, messages and uploaded documents.
Human-in-the-Loop Review: Ensured that employees could review, correct and approve AI-generated information before any order was confirmed.
Business System Validation: Planned integration with AutoCount to validate customer accounts, credit limits and live inventory availability.
Assisted Communication: Introduced AI-generated customer reply suggestions with selectable response tones for staff to review and edit.
Centralised Monitoring: Designed dashboards and task queues to provide clear visibility into order statuses, pending actions and operational bottlenecks.

Business and User Impact

Business Impact

Reduced the amount of time spent reviewing and entering order information manually
Improved consistency and accuracy across order-processing workflows
Created a more scalable process for managing increasing enquiry and order volumes
Reduced operational dependency on individual employees’ knowledge and working methods
Improved visibility into pending orders, exceptions and processing bottlenecks

User Impact

Gave employees a single workspace for managing incoming enquiries and orders
Reduced repetitive administrative work and manual data entry
Allowed users to review and approve AI-generated information instead of creating every order from the beginning
Provided faster access to relevant customer, credit and inventory information
Simplified customer communication through editable AI-generated reply suggestions
Helped employees respond to customers more quickly while maintaining human oversight

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