Bringing Voice Commands into Construction ERP – The Maatop Case

Why this project?

Maatop is a user-friendly ERP and office solution designed for the earthmoving sector. It aims to make it easy for contractors to manage office routines within the system, even while sitting in an excavator. The project’s objective was to apply AI so that creating offers and processing orders would be possible via voice recognition directly from the operator’s cab. Instead of typing details into a screen, the driver could enter them by speaking. This saves time and enhances ease of use.

Company Introduction

Maatop Oy is a Finnish software company specializing in the automation of earthworks operations as well as the management and support of related processes. The company’s expertise in ERP systems dates back nearly ten years, and earthmoving companies have been its primary customer group from the very beginning.

What we did in the project

The technical and functional requirements were defined.
We analyzed the technical limitations of the current solution and defined the necessary new technologies, data, and API interfaces.
We developed and validated a new AI-assisted speech recognition feature.
The solution’s functionality was tested, and the findings were documented.

Connection to AI skalaajaat

The project is part of the AI skaalaajat initiative, which supports SMEs in Uusimaa by providing expert services under the de minimis aid framework. The initiative was carried out in collaboration with Haaga-Helia’s Softala.

Results/Benefits

The project provided Maatop Oy with a proof-of-concept effort where speech recognition integration into the Maatop ERP system was specified, developed, and tested. The outcome was a working prototype that allows voice control to be added to ERP more quickly, should customers wish to adopt it. This means that entering new data into earthmoving operations can be done faster and more naturally within work processes.

Initial Mapping and Current State Assessment

In the first phase of the project, the current AI capabilities of the participating SMEs are assessed. This includes:

Defining the companies' AI-related goals and needs.

An AI maturity analysis to evaluate the company's readiness and capabilities to utilize AI in business.
Identifying the companies' business environment and technological challenges.

The goal is to form a clear picture of the companies' starting point and define the target state to be achieved with AI solutions.

AI Coaching and Workshops

In the second phase, companies participate in iterative training sessions and workshops. During these, the focus is on the following topics:

AI fundamentals and opportunities: How can AI create added value?
Proof of Concept (POC): Companies are provided with a concrete model for utilizing AI in a small-scale pilot project.
Multidisciplinary capabilities: How to combine expertise and technologies from different fields with the help of AI?

The workshops support companies in ideating and testing concrete AI solutions, while also providing an opportunity to network with other companies and experts.

Solution Concept Development and PoC Projects

At this stage, we move on to company-specific PoC projects, in which:

• The first AI solution models are put into practice in company business environments.

• The developed concepts are tested and validated in practice.

• Collaboration and expert support are utilized.

Minimum Viable Product (MVP): Each company will have a basic version of an AI solution developed, serving as the minimum viable product (MVP) to allow efficient testing before large-scale implementation.

Development of the Overall Concept and Scaling

At this stage, the lessons learned from the PoC projects are integrated into a tailored 'Whole AI Service' concept for each company, which includes:

Processes and models needed for the implementation and management of AI solutions.
Strategies for utilizing multidisciplinary networks.
Guidelines for scaling and integrating technologies into business processes.

The concept provides companies to scale and utilize AI solutions effectively in the long term.

Co-creation and Sharing Results

The last phase of the project highlights co-creation and learning:

Companies share the experiences gained during the PoC projects within their networks.
Workshops are organized for companies and experts to develop solutions together.
The project results are communicated and best practices are shared more broadly.
At the same time, future development opportunities are identified and it is ensured that the participating companies can continue to utilize AI solutions after the project ends.