How Trackunit turns construction data into decisions with AI

How Trackunit turns construction data into decisions with AI | Databricks Blog Skip to main content

Summary

Trackunit’s IrisX is an operating data platform built on Databricks for connecting construction equipment and operational data.

It addresses fragmented, inconsistent data across OEMs, rental companies, contractors, EPCs, owners, and developers.

By turning machine signals into contextual intelligence and embedded workflows, it helps teams make faster decisions about uptime, service, utilization, billing, and asset value.

Construction generates abundant data, from equipment telemetry and maintenance records to job-site documents and rental feeds. But like manufacturing, the industry faces fragile supply chains, margin pressure, and rising demands for speed, customization, and traceability. The data remains fragmented across systems, organizations, and equipment types. Ownership can change from project to project, and critical information may be unstructured, disconnected, or never captured. This is more than a data quality problem; it is a data intelligence problem. AI cannot improve decisions when data is never connected, structured, or provided with the right operational context. Trackunit is addressing this challenge with IrisX , an operating data platform built on Databricks Data and AI Platform. Trackunit’s value lies in its construction-specific context: connecting equipment, machine, operator, site, and operational data across the ecosystem so teams can turn fragmented signals into insights and action. That perspective is grounded in 20 years of industry experience, 5,000 customers, 6 million connected assets across 120 countries, 1,200 connectors, and 150 partner marketplace applications. By leveraging Databricks, Trackunit can bring data engineering, analytics, and AI together on a single, open platform. This enables IrisX to turn raw machine data into operational decisions and enterprise intelligence through three capabilities: connect, distill, and amplify. Watch Domokos Spéder, VP, Commercial EMEA & Global Consulting at Trackunit, and Erin Kirsten, Manufacturing Senior Solutions Architect at Databricks, share how Trackunit was built on Databricks.

Connect fragmented data across the construction ecosystem Construction equipment does not operate in isolation. Original equipment manufacturers (OEMs) need visibility into how machines perform after they leave the factory. Rental companies need to understand utilization, availability, and maintenance across locations. Contractors need to know where equipment is working, where it is underused, and where capacity is constrained. Each organization has different systems and priorities. They may also use different data models and terminology. A machine’s telemetry may live in one platform, service records in another, and job-site information in a document or spreadsheet. Without a shared foundation, even a straightforward operational question can require manual reconciliation. IrisX brings together data from machines, equipment, operators, documentation, and third-party sources. The platform is designed to work with the tools that construction businesses already use, including tracking applications, enterprise resource planning systems, customer applications, and analytics or AI engines. This foundation preserves the context behind each signal. A fault code is more useful alongside equipment history, operating conditions, maintenance activity, and location. Utilization becomes more useful when compared with contract terms, project needs, and asset availability.

An overview of Trackunit’s single platform

Distill machine signals into usable intelligence Connecting data is only the first step. Raw equipment data is not automatically useful to a product manager, service team, fleet operator, or business leader. It must be cleaned, structured, governed, and translated into questions the business can act on. IrisX applies construction-specific context to that process. In the demo above, a user asks how engine load and torque affect fuel consumption and which equipment cohorts are outliers. The system returns an executive summary, visual analysis, and equipment-type breakdown without requiring a query. A second example examines regional operating hours and seasonal patterns, helping product and engineering teams design for field reality rather than an assumed average. This change determines who can use advanced analytics, allowing product managers, service teams, and operations users to ask natural-language questions about their fleet and operations and receive answers grounded in governed data. This extended access enables timely intelligence wherever decisions are made. The same intelligence can also be made available through the tools people already use. Through the Trackunit IrisX MCP , Trackunit can embed IrisX analytics in Trackunit Manager, so users can get answers and take action in their preferred AI tools without switching systems or moving data around. Amplify insights through operational workflows An insight only creates value when it changes what happens next. That is the purpose of the amplify layer: turn signals into decisions and decisions into action. This is why Trackunit built IrisX Blueprints , ready-to-deploy solutions for specific construction and equipment use cases. They combine data connections, workflows, analytics, and AI-driven automation logic so teams can start from a business problem rather than a blank development environment. These blueprints are deployable in days rather than months, without custom development, helping teams move from connected data to action faster. Help OEMs improve equipment and battery decisions For OEMs, field data can inform product design, service operations, warranty management, and digital offerings. A Battery Management Insights Blueprint can consolidate charging behavior, standby time, battery status, and reporting activity across electrified equipment. Teams can see reporting status and charge levels across the fleet, identify low-charge or inactive assets, and inspect charging sessions over time. The demo above describes a customer who found a pattern of short, shallow charging sessions associated with premature battery degradation. That insight supported better charging guidance, while the same data can inform fleet readiness and battery transparency for resale or buyback. For an OEM fleet of roughly 10,000 machines produced per year, the battery management...