The recent surge in Artificial Intelligence (AI) solutions has led to unprecedented innovation in software, making the selection of software tooling much more complex. Even now, one thing is already becoming clear: software vendors that want to use AI effectively must strike a balance between innovation and reliability.
This balance between innovation and reliability is determined by the business outcomes achieved through the use of AI. Simply applying AI techniques — such as generative AI (GenAI), large language models (LLMs), Deep Learning, or Machine Learning — does not solve today's MRO challenges. It is part of the story, but not the most important part.
We choose the right tool for the right purpose and use it the right way
Claiming to use an AI to improve the quality of your spare parts data is like a restaurant saying that its kitchen uses “advanced baking techniques” to make your dessert tasty. In MRO or a restaurant's kitchen, what generates value is how those AI techniques are applied and what outcomes they achieve.
That’s why we do not focus on the AI techniques themselves but on their application to achieve an advantageous outcome for our customers.
Since our early days, we have focused on delivering accurate spare parts data and insights that MRO professionals can rely on without compromising ease of use. This has been a cornerstone of our success and our customers’ trust in our product.
We use AI to eliminate inaccuracies so you can trust your data to drive smarter, more efficient decisions.
We know that accuracy matters most when it comes to MRO spare parts data. For example, it’s not enough to have just the article number for an SEW engine — you also need the serial number to ensure you're ordering the exact right part. Without this accuracy, organizations struggle to optimize inventory, control spending, make informed decisions, and keep operations running smoothly.
To illustrate, let’s look at our proprietary MRO data enrichment technology.
Upload your material master to our platform. With strategically chosen AI techniques, our technology will compare your spare parts records with those in our spare parts catalog, which originates from the respective original manufacturer of each given part. We have invested heavily in providing high-accuracy suggestions to deliver only verified and trustworthy matches. Based on these verified matches, the system makes final suggestions for improving the relevance of your material master data.
These reliable suggestions fall into three categories.
A natural question is how we ensure the accuracy of our suggestions, given the variability in AI reliability. The answer lies in our strategic implementation.
We have built SPARETECH’s verified global spare parts catalog, drawing from original part manufacturers, historical records, and a range of offline and online sources. This ensures that every part in our system is real and accurate. As a result, SPARETECH software never matches your records with non-existent parts or makes inaccurate recommendations for enriching data, removing duplicates, or flagging obsolete parts.
Instead of overwhelming users with a mix of accurate and inaccurate suggestions and requiring manual validation, SPARETECH delivers only final, verified, and reliable recommendations. We then give the MRO professional conscious control over whether to accept these suggestions as needed, ensuring efficiency without sacrificing accuracy.
Georgi Staykov
VP Engineering | SPARETECH
It’s a win-win: SPARETECH uses AI to speed up tedious records-management work, and MRO professionals can check the system’s work to ensure 100% accuracy.
Generative AI (GenAI) is useful in applications that require creativity and collaboration to offer the greatest possible assistance to the MRO professional.
In keeping with our goal of helping customers attain the greatest possible accuracy for their spare parts data, our use of LLMs is strategic. We use them in targeted processes where potential inconsistencies, such as an LLM’s tendency to “hallucinate” by making up information, won’t cause critical issues.
We turn the AI technique into an effective collaborator by optimizing it for creativity and adaptability in a restricted context.
Our latest Large Language Model (LLM)-powered feature is an excellent example of this strategy: The system scans an existing record, and the LLM uses spare parts data to categorize the record accurately according to industry standards, such as ECLASS. Then, the system uses this data to generate a description and suggests it to the user. The MRO professional can now review the description and make any necessary edits.
From the company's perspective, this AI implementation empowers our users to quickly create standardized descriptions that can be applied to the global material master. The business outcomes are a more efficient workforce and improved data quality.
This is an overview of how SPARETECH uses AI today. However, our integration goes much deeper and evolves as AI continues to develop.
A balanced approach gives us the confidence to increase our investments in relevant AI technologies in order to help us serve our customers better.
At the same time, we remain committed to the principles outlined above — selecting the right AI techniques and applying them in the right way — to further enhance our MRO software and deliver tangible business results for our customers.