Intelligent Search
At Niko, machine failures weren’t new problems, they were just hard to find in a mountain of unstructured maintenance data. With over 88,000 tickets and 10,000+ pages of manuals, engineers spent more time searching than fixing. By building a structured, measurable knowledge platform before adding AI, Intodata helped Niko turn fragmented information into operational intelligence.
Niko, a leading Belgian manufacturer of electrical installation materials, faced a recurring issue on the production floor. Machine failures were often not new problems—solutions already existed within the organisation, but they were difficult to find.
Maintenance teams spent valuable time searching through legacy tickets and scattered documentation, slowing down resolution times and increasing downtime. What should have been a quick fix often led to unnecessary troubleshooting and delayed resolutions.
1
Reduced machine downtime through faster issue resolution.
2
Improved efficiency by unlocking existing maintenance knowledge.
3
Increased equipment effectiveness with better access to insights.
Niko’s systems contained a wealth of information: over 88,000 maintenance tickets and more than 10,000 pages of manuals. However, this data was largely unstructured and inconsistent.
Ticket entries varied in terminology, writing style, and completeness. Misspellings, abbreviations, and missing root cause analyses made it nearly impossible to efficiently retrieve relevant information.
As a result, valuable knowledge remained hidden, leading to:
Traditional search tools couldn’t handle the complexity of this data, which was unstructured and inconsistent. The situation demanded a solution capable of:
However, migrating all data to a generic AI assistant was not a workable option. Without granular control over search parameters, sorting logic, data access permissions, and result transparency, the system would effectively function as a black box: impossible to validate,evaluate, or systematically improve.
Intodata developed a structured, measurable methodology to guide Niko from fragmented, unstructured data to a production-grade knowledge platform, where AI helps engineers quickly retrieve the right information. Instead of jumping straight into AI, Intodata took a structured approach focused on control and reliability.
A five-step methodology was implemented to transform scattered data into a trusted knowledge platform:
This approach ensured that the system remained explainable, measurable, and continuously improving—avoiding the risks of a black-box solution.
By making maintenance knowledge accessible and usable, Niko achieved a clear operational impact:
What was once fragmented information is now a reliable source of operational intelligence—directly supporting teams on the production floor.
.jpg)
%20copy.webp)
