From Legacy Middleware Debt to AI Innovation: Rebuilding the Digital Backbone of a 150-Year-Old Manufacturer

Summary

A global manufacturing client was facing rising middleware costs, poor visibility, and growing pressure to support analytics and AI initiatives. A forced three-year middleware commitment became the trigger to rethink their integration strategy. This article shares how the client moved away from legacy middleware, reduced integration costs by nearly 95%, improved operational visibility, and built a strong data foundation for the future.

Table of Contents

• 1. The Middleware Cost Problem
• 2. Building a New Integration Setup
• 3. Making Integrations Visible
• 4. Preparing Data for AI
• 5. How We Did It
• Savings Metrics

The client was running critical integrations on a legacy middleware platform that had gradually become a financial and operational burden. Licensing costs increased sharply, with annual fees rising from $20,000 to $50,000 and a mandatory three-year commitment pushing the total to $160,000.

Despite the cost, visibility remained limited. Integrations behaved like black boxes, failures were difficult to trace, and teams relied on manual intervention to diagnose and fix issues.

At the same time, the business was pushing toward better reporting, analytics, and AI-driven insights. These initiatives required clean and reliable data flows that the existing middleware could not provide efficiently.

Legacy middleware and Scribe-based integrations were replaced with Azure Logic Apps and Azure Functions. The new setup was designed to support global operations across multiple legal entities.

Separate DataAreaIDs were maintained for regions including TOUS, TOUK, TOIN, and TOCN. Branching logic handled country-specific account number mappings such as cf_accountnumberus and cf_accountnumberuk.

An agentless architecture was adopted using Azure Blob Storage with Logic Apps. This removed firewall and SQL connectivity challenges and eliminated reliance on unsupported personal-mode gateways.

The previous setup offered no centralized monitoring, making it difficult to detect failures early.

A Power BI dashboard built on Azure Log Analytics provided a clear view of integration health and execution status.

Automated alerts were configured to notify teams within one hour of failures, allowing issues to be addressed before impacting critical business processes.

With stable integrations in place, the focus shifted from cost savings to long-term readiness.

Clean data flows became the foundation for platforms such as Databricks and governance layers like Unity Catalog.

The architecture supports conversational AI use cases, enabling questions like “Is raw material available for this production order?” to be answered from a unified data foundation.

As a first step, 32 reports were consolidated into a single catalog to validate data quality and integration reliability.

1. Retrieve config.json and checkpoint.txt from Azure Blob Storage for configuration and state control.
2. Run incremental HTTP GET queries using ModifiedDateTime1 gt [CheckpointTimestamp].
3. Check for existing records using OData queries in target systems with keys such as ScribeCRMKey.
4. Transform data using Azure Functions with region-specific Liquid templates.
5. Write data securely using PATCH or POST operations with OAuth 2.0 authentication.
6. Update checkpoint timestamps in Azure Blob Storage after successful execution.
7. Log step-level success or failure using a centralized Logging Logic App (TO-UAT-Logs).

• 95% reduction in annual integration costs, from $50,000 to approximately $2,555.
• Approximately $140,000 in annual savings.
• Integrations across D365 Field Service, D365 Sales, D365 Finance & Operations, Shopify, and SQL Server.
• Designed to support modernization of more than 600 fragmented reports.

Q: How does this impact Shopify integrations?
A: Azure Integration Services acts as the middle layer, enabling Shopify orders to synchronize into Finance & Operations and CRM systems in real time.

Q: Is the system secure for global entities?
A: Yes. The solution uses Azure AD OAuth 2.0 and centralized key management for all API calls.

Q: Can it handle attachments?
A: Dedicated Logic Apps were designed to synchronize CRM annotations and attachments to SQL servers located behind firewalls using an agentless architecture.