{"id":52930,"date":"2026-06-09T07:00:00","date_gmt":"2026-06-09T05:00:00","guid":{"rendered":"https:\/\/theark.ch\/?p=52930"},"modified":"2026-09-18T15:49:35","modified_gmt":"2026-09-18T13:49:35","slug":"with-the-help-of-the-ark-neuralia-and-sta-are-developing-an-ai-solution-for-industrial-maintenance","status":"publish","type":"post","link":"https:\/\/theark.ch\/en\/news\/with-the-help-of-the-ark-neuralia-and-sta-are-developing-an-ai-solution-for-industrial-maintenance\/","title":{"rendered":"With the help of The Ark, Neuralia and STA are developing an AI solution for industrial maintenance"},"content":{"rendered":"\n
The Valais-based startup Neuralia, headquartered in Sion, and the SME Services Techniques Alpins SA (STA), which specializes in industrial equipment maintenance in Sembranch, have collaborated on an innovative project supported by The Ark Foundation. The goal: to develop an artificial intelligence solution capable of assisting technicians in diagnosing breakdowns by leveraging service history data and technical manuals. With an average rating of 3.4\/5 from operators during testing, this prototype demonstrates AI\u2019s potential to preserve technical expertise and accelerate the skill development of new employees.<\/strong><\/h5>\n\n\n\n

Like many companies active in technical maintenance and repair, STA<\/strong><\/a> faces a critical skills shortage. \u201cSenior technicians possess valuable expertise, but this expertise is gradually disappearing as they leave,\u201d explains Lionel May, director of STA. \u201cConversely, junior technicians struggle to quickly develop their skills and effectively diagnose breakdowns early in their careers.\u201d
Faced with this reality, STA sought a solution to leverage data from breakdowns to assist its technicians in their diagnostics. \u201cWe needed to find a way to capitalize on the experience we\u2019ve accumulated over the years,\u201d says Lionel May. \u201cThe idea was to transform this knowledge into a practical tool accessible to all our technicians.\u201d
It was in this context that the project with Neuralia<\/strong><\/a> began, with the goal of combining service history, technical manuals, and artificial intelligence to create a diagnostic assistant.<\/p>\n\n\n\n

Three Main Objectives<\/strong><\/h5>\n\n\n\n

The project had three main objectives, according to Gary Folli, founder of Neuralia. \u201cFirst, to troubleshoot faster and more effectively by guiding technicians to the right solution at the right time. Second, to preserve expertise before it disappears by capturing the know-how of senior technicians. Third, to accelerate the skill development of junior technicians, enabling them to become more independent more quickly.\u201d
For Lionel May, the stakes were particularly high in the current context. \u201cThe shortage of skilled labor is forcing us to rethink our work methods. We can no longer afford to lose years of expertise every time someone retires.\u201d
The project began with a needs assessment, followed by a proof of concept (PoC) carried out with the support of The Ark. \u201cWe utilized the fault databases as well as the machine manuals, which we combined with AI,\u201d explains Gary Folli.<\/p>\n\n\n\n

Extensive Testing with Operators<\/strong><\/h5>\n\n\n\n

Once the prototype was developed, it was extensively tested by STA operators. \u201cTechnicians could leave notes and comments on the responses generated by the AI. \u201cThis feedback allowed us to gauge satisfaction and identify areas for improvement,\u201d explains Gary Folli.
In total, 53 failure cases were tested, with an average rating of 3.4 out of 5. \u201cThe overall user feedback is clear: the prototype is already usable and useful in the field as is. This is particularly promising, as there is still significant room for improvement,\u201d emphasizes the founder of Neuralia. For Lionel May, these results confirm the solution\u2019s potential. \u201cOur technicians were pleasantly surprised by the relevance of the responses. Even though this is just the beginning, the tool is already providing them with concrete assistance in their daily work.\u201d<\/p>\n\n\n\n

Technical Challenges Overcome<\/strong><\/h5>\n\n\n\n

The project was not without its challenges. \u201cThe main obstacles involved data quality and structure, the AI\u2019s ability to interpret highly specialized data related to machine breakdowns, and choosing the right technical approach,\u201d explains Gary Folli.
A key lesson from the project is that in AI, \u201cyou should never judge a project solely on initial assumptions. The only reliable approach is to test it in practice with a prototype and have it evaluated by domain experts,\u201d he notes. \u201cIn this specific case, it wasn\u2019t clear at the outset that AI would have such a significant impact in a manual line of work. \u201cWe tend to wrongly underestimate these use cases compared to administrative or cognitive roles.\u201d<\/p>\n\n\n\n

The Pivotal Role of The Ark<\/strong><\/h5>\n\n\n\n

\u201cThe Ark played a key role throughout the project by providing financial support, which made it possible to launch the project despite initial uncertainties,\u201d emphasizes Gary Folli. \u201cBut beyond the financial aspect, the strategic and technical advice provided was invaluable at every stage: features, product direction, and technical choices.\u201d
For Lionel May, this support was crucial. \u201cWithout The Ark\u2019s support, we probably wouldn\u2019t have dared to embark on such a project. Their expertise allowed us to move forward with confidence.\u201d<\/p>\n\n\n\n

Toward a Comprehensive Platform: Maintrix<\/strong><\/h5>\n\n\n\n

Buoyed by these encouraging results, the project doesn\u2019t stop there. \u201cThe goal is to transform this prototype into a comprehensive platform, which will be called Maintrix,\u201d announces Gary Folli. \u201cTests are currently underway at other companies facing the same diagnostic challenges, with equally encouraging results.\u201d<\/p>\n\n\n\n

At a Glance<\/strong><\/h5>\n\n\n\n

Amount invested by The Ark Foundation:<\/strong> CHF 10,000<\/p>\n\n\n\n

Project duration:<\/strong> 8 months (April 2025\u2013December 2025)<\/p>\n\n\n\n

Partners<\/strong>: Neuralia, STA, Cimark, The Ark<\/p>\n","protected":false},"excerpt":{"rendered":"

neuralia and services techniques alpins sa (sta) have collaborated on an innovative project supported by the ark foundation. the goal: to develop an artificial intelligence solution capable of assisting technicians in diagnosing breakdowns by leveraging service history data and technical manuals.<\/p>\n","protected":false},"author":3,"featured_media":51738,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[207],"tags":[],"class_list":["post-52930","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digitalisation"],"acf":[],"_links":{"self":[{"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/posts\/52930","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/comments?post=52930"}],"version-history":[{"count":1,"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/posts\/52930\/revisions"}],"predecessor-version":[{"id":52931,"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/posts\/52930\/revisions\/52931"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/media\/51738"}],"wp:attachment":[{"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/media?parent=52930"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/categories?post=52930"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/theark.ch\/en\/wp-json\/wp\/v2\/tags?post=52930"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}