Equipment hire & fleet compliance — multi-portal architecture and legacy-data migration

Anonymised by industry. The client’s identity is withheld pending consent.

Industry shape

Mid-market equipment hire business with heavy-vehicle and specialty-asset fleet across multiple depots. Customers include large industrial accounts requiring per-customer billing groups, rotating purchase orders, and inspection-trail compliance. Legacy operations on an Auto-IT-based industrial scheduling system plus spreadsheets plus three separate billing tools.

What the platform replaced

• Spreadsheet-based fleet asset register with no expiry / certification tracking
• Three separate billing tools (one for hire, one for service, one for parts) with no consolidation
• Per-customer pricing maintained in salesperson-specific Excel sheets
• Inspection workflows on paper forms, scanned and emailed
• No customer self-service for booking, status, or invoice access

What XCentral delivered

• Cross-portal identity layer with multi-portal access — depot operator portal and customer portal as separate deployments, sharing identity and backend API per the framework’s portal-separation pattern
• Asset register with per-asset agreements, inspections, expirations, certifications
• Per-customer billing groups with rotating purchase orders and consolidated billing
• Inspection workflow with photo capture and audit trail
• Azure Data Factory + Self-Hosted Integration Runtime pattern for legacy Auto-IT integration — moving legacy operational data into the new platform without disrupting the running legacy system
• Customer portal with self-service booking, status visibility, invoice access

Velocity

• Phase 1 (Customer Account Records) + Phase 2 (Asset Management) + Finance integration + Group Tenant Service Phase 1A — delivered in sequential 4–6 week phases
• ADF data flows for legacy system integration produced first-day-of-Phase-1 production data flow

Data migration scope

• A multi-million-row general ledger from the on-prem legacy system migrated into Azure SQL, with transform-layer filtering to fit the platform’s canonical data model.
• Chart-of-accounts data across all company entities, with operator-prepared classifications applied at import (designed-in curation, not afterthought rework).
• The platform’s full operational entity set across the customer-asset-registration-inspection-insurance-agreement-purchase-order chain.

On migration speed specifically

“What traditionally takes a data engineer weeks — schema discovery, integration runtime setup, ADF pipelines, transform procs, error handling, deployment — happens in days inside the framework. The pattern is codified, not invented per engagement.”

• Faster than rebuilding the integration plumbing per project: the Azure Data Factory + Self-Hosted Integration Runtime pattern is framework-canonical, not invented for this engagement.
• Cleaner than typical migrations: deduplication, validation reporting, and rollback SQL are pattern outputs.
• Better aligned: data lands shaped to the platform’s canonical model rather than the model adapting to accommodate legacy quirks.

Hours comparison

Compression: 3.5–5×.

Multi-portal architecture is binding under the framework — no rebuild work to figure out the right portal separation pattern. ADF + SHIR pattern is documented in framework operations — no R&D time on the integration approach.