---
title: "Volvo AI Assistant"
description: "Volvo AI Assistant (Volvo Construction Equipment, 2024/25): Operators fix simple machine faults themselves, guided by AI and the camera"
url: "https://www.sugar-network.org/en/projects/volvo-ai-assistant-2024-25/"
lang: "en"
---

# Volvo AI Assistant

**Operators fix simple machine faults themselves, guided by AI and the camera**

_Meet jack, Your new AI Assistant - Allows you to keep your machines UP and running_

- **Corporate partner:** [Volvo Construction Equipment](https://www.sugar-network.org/en/network/partners/volvo-construction-equipment/) (https://www.volvoce.com/)
- **Cohort:** [2024/25](https://www.sugar-network.org/en/projects/cohort/2024-25/)
- **Theme:** [Industry & operations](https://www.sugar-network.org/en/projects/themes/industry-operations/)
- **Type:** Digital service
- **Key fact:** > 50 % Share of on-site machine issues that are simple
- **Universities:** [Blekinge Institute of Technology](https://www.sugar-network.org/en/network/universities/bth/), [University of São Paulo](https://www.sugar-network.org/en/network/universities/usp/)
- **Team:** 6 master students
- **Disciplines:** Computer Engineering, Computer Science, Electronics Engineering, Industrial Engineering, Mechatronics

## Challenge

On remote construction sites a mechanic can take hours or days to arrive after a breakdown. Because tasks depend on each other, one failed machine can halt the whole operation and drive up costs.

## What the team learned

More than half of machine issues on construction sites are simple and could be fixed in minutes, yet they still trigger a call to the mechanic.

## What they built

Jack, an AI assistant for machine operators. It walks them through a guided diagnosis based on Volvo's official technical documentation and uses computer vision to detect and interpret signs of malfunction, so mechanics are called only when needed.

## Results

- Projected: operators resolve more than half of machine issues without outside help.

## Impact

For Volvo CE, Jack would collect real-world data on recurring failures and site conditions to refine maintenance practices and inform future machine design.

## Full project text

Instant, on-site troubleshooting for every stage of your machine’s life cycle

### Challenge

One of the major challenges in construction sites – especially those in remote locations – is equipment downtime. When a machine fails, it can take several hours or even days for a mechanic to arrive, due to limited access and logistical constraints. This delay often triggers a chain reaction: since many tasks are interdependent, the breakdown of a single machine can halt the entire operation, leading to significant productivity losses and increased costs. As infrastructure projects become more complex and time-sensitive, reducing downtime through smarter, faster solutions becomes critical for the efficiency and sustainability of the construction industry.

### Solution

More than 50% of machine issues on construction sites are simple and could be resolved in just a few minutes—yet they still trigger a call to the mechanic. This not only wastes valuable time but also contributes to unnecessary downtime, especially in remote areas where support can take hours to arrive. Our solution, Jack, is an AI-powered assistant that empowers machine operators to handle these basic issues independently. Using a guided diagnostic process based on Volvo’s official technical documentation, Jack combines artificial intelligence with computer  
vision to detect and interpret signs of malfunction. This enables fast, accurate troubleshooting through an intuitive interface, helping keep operations running and ensuring that mechanics are only called when truly necessary.

### Impact

Jack delivers measurable impact by significantly reducing equipment downtime, leading to fewer project delays and lower operational costs for users. By enabling operators to resolve over 50% of issues without external help, the solution increases autonomy and improves on-site efficiency. For Volvo CE, Jack offers a strategic advantage by collecting real-world data on recurring failures and site conditions. These insights are crucial for refining maintenance practices and even rethinking machine design to prevent similar issues in future models. Ultimately, Jack fosters a more intelligent, integrated, and resilient construction ecosystem.

## Who built it.

- Maja Svensson, Industrial Engineering and Management (Blekinge Institute of Technology)
- Ahmed Mahfooz Ali Khan, Computer Science Engineer (Blekinge Institute of Technology)
- Madhav Sai Aryan Kothareddy, Electronics and Communication Engineering (Blekinge Institute of Technology)
- Helena Moyen, Computer Engineering (University of São Paulo)
- Murilo Monteiro, Industrial Engineering (University of São Paulo)
- Leonardo Valente, Mechatronics Engineering (University of São Paulo)

## Sources

Summarised by the SUGAR editorial team from the team’s project page.

Figures marked “Projected” are the team’s own estimates, not measured results.

Open the 2024/25 booklet: https://drive.google.com/file/d/1xTUQoA8DWtrKZirgI_On_TPranx_BzUi/view?usp=sharing