AI-Powered Field Service Operations

A PE-Backed Energy Services Leader Unifies Post-M&A Operations with AI

Field service intelligence across petroleum and EV infrastructure.

Acquired companies

1,400

Technicians

One

Unified intelligence platform

About

A PE-backed national infrastructure services company formed through the rapid consolidation of seven regional providers was operating across 21+ offices and a 1,400-person field workforce with fragmented financial systems, delayed invoicing, and manual dispatch processes inherited through M&A. Veltris unified the data and operational intelligence layer across the national footprint, combining AI-powered field service optimization, RPA billing automation, and predictive maintenance.

From post-M&A complexity to one connected operating model.

Challenge

Turning M&A complexity into a scalable national operation.

Fragmented financial and operational systems inherited through M&A created delays in national invoicing, revenue cycle management, and technician dispatch across 21+ offices. As the company expanded into EV charging infrastructure, the lack of a unified data and operational intelligence layer threatened profitability and service quality at scale.

PAIN POINTS

Fragmented systems across acquired entities
Delayed national invoicing and revenue cycle management
Manual technician dispatch and scheduling
Reactive maintenance across petroleum and EV assets

Solution

AI-powered operations built for a growing field service network.

Veltris consolidated financial and operational data from multiple acquired entities into a unified cloud-native data lakehouse, enabling standardized KPI visibility and real-time reporting across the portfolio. RPA-driven billing automation reconciled fragmented legacy ERP systems, accelerating revenue cycle management and eliminating manual invoicing delays.

AI-powered field service routing and dispatch optimization was built for the 1,400-person technician network, dynamically matching petroleum and EV specialization with work order priority and geography. Predictive maintenance pipelines ingested sensor data from fuel dispensers, POS systems, and EV chargers, shifting operations from reactive emergency service toward proactive asset management.

Step Flow
Unify → Automate → Optimize → Predict → Scale

Connected intelligence turns fragmented operations into proactive field service.

WHAT CHANGED IN THE WORK

Fragmented M&A systems → Unified data lakehouse
Manual invoicing → RPA-driven billing automation
Manual dispatch → AI-powered field service optimization
Reactive maintenance → Proactive asset management

WHAT WE DELIVERED

Cloud-Native Data Lakehouse
RPA-Driven Billing Automation
AI Field Service Routing & Dispatch
Predictive Maintenance Pipelines

Ready to turn post-M&A complexity into intelligent field operations?

Results

One intelligence layer. Smarter field operations. Greater scale.

KEY RESULTS DELIVERED

Faster

invoice consolidation with accelerated national reconciliation and reduced manual effort

Higher

technician utilization through improved dispatch efficiency across a 1,400-person workforce

Proactive

uptime management across petroleum and EV charging assets, reducing emergency service dependency

Why Veltris

Veltris combined cloud-native data engineering, AI-powered field service optimization, automation, and predictive intelligence to unify operations across a rapidly consolidated infrastructure services organization. By connecting acquired systems, field operations, and asset data within one intelligence layer, Veltris helped transform post-M&A complexity into a scalable operating model across petroleum and EV infrastructure.

CAPABILITIES APPLIED

Cloud-Native Data Lakehouse Engineering
AI Field Service Routing & Dispatch
RPA-Driven Billing Automation
Predictive Maintenance
Post-M&A Systems Consolidation
EV & Petroleum Infrastructure Intelligence

We were transitioning from a single company operating model to a Multi Business Unit (Multi BU) model. As part of this transformation, Veltris has remediated approximately 400+ SSRS reports and underlying business processes to support BU-level data segregation/aggregation and reporting, leading to the accuracy of financial and business operation data. This initiative reduced month-end financial closing time by ~40%, improved reporting accuracy to 99.8%, and enabled autonomous BU operations with centralized governance.

Chief Operating Officer A global energy and infrastructure services company

Connect field operations, enterprise data, and asset intelligence with Vertical AI.