Frequently asked questions

AI-powered maintenance is the use of artificial intelligence to support professionals with maintenance, repairs, and fault resolution. The AI evaluates available information and provides it in the context of the task at hand. Pairio applies this approach specifically to industrial maintenance.

Artificial intelligence can support information retrieval, troubleshooting, documentation, analysis of historical faults, spare-parts identification, and the creation of work instructions. Pairio supports the flow of information from fault reporting and repairs through to final documentation. Asset, maintenance-plan, and work-order management can remain in the existing ERP or CMMS.

Pairio supports troubleshooting by making technical documentation and existing maintenance knowledge accessible faster. Technicians can ask specific questions, recognize known fault patterns, and draw on previous repairs. This helps resolve faults faster and reduce downtime.

Maintenance professionals can ask questions by typing or speaking in their preferred language. Pairio searches manuals, operating instructions, wiring diagrams, and other technical documents for relevant information about symptoms, components, or diagnostic steps and links each answer to its source.

Maintenance professionals can capture their work through voice, photos, or video. Pairio identifies details such as symptoms, completed actions, and spare parts, structures the information, and turns it into a service or maintenance report. Documentation is created during the work instead of being entered manually afterwards.

Pairio uses the specific asset, the reported symptoms, and the connected technical documents to narrow down the search. It brings the relevant part information and source documents together in one place, helping maintenance professionals identify the required spare part without searching multiple systems individually.

Pairio preserves information that employees capture during maintenance, faults, and repairs and assigns it to the relevant asset context. Each case expands a shared knowledge base. Practical expertise remains available across the company, reducing dependence on individual specialists.

Pairio reduces the workload on skilled professionals by simplifying information retrieval, documentation, and work instructions. At the same time, practical knowledge is systematically preserved and made accessible to less experienced employees. Experts can work more efficiently, while new staff become productive faster.

Yes. Pairio can use recorded workflows to create structured work instructions. Tasks can, for example, be captured via video or voice and automatically converted into a usable format. This allows practical maintenance knowledge to be standardized, shared, and reused more efficiently.

Pairio is designed for maintenance professionals, technicians, and other employees in production environments. No specialized AI training is required, and less experienced employees can use it to find relevant information and instructions. The software does not replace professional qualifications, company approvals, or applicable safety requirements.

No. Pairio is not intended to replace skilled professionals but to make knowledge scalable and reduce routine work. Experts can document their knowledge more effectively while other employees gain faster access to relevant information. This reduces dependence on individual specialists.

Operating manuals provide technical knowledge from the manufacturer, while historical fault data reflects experience from the company’s own operations. Pairio can make both sources usable for troubleshooting. Data from similar machines can provide additional context when available in the connected data set.

Pairio helps analyze historical service and maintenance data. Recurring faults, repairs, and notifications can be examined for patterns and optimization opportunities. This helps maintenance managers identify machines or components that cause problems particularly often.

Pairio supports the analysis of historical maintenance data and can provide indications of appropriate maintenance intervals. Preventive maintenance strategies can therefore be based more closely on actual experience and available data instead of relying solely on fixed schedules.

Pairio supports data-driven and forward-looking maintenance by analyzing historical maintenance data and revealing patterns. Predictive maintenance in the narrower sense may additionally require machine, sensor, or condition data.

Yes. Pairio can also assist with faults that occur for the first time, provided relevant technical information is available. The AI can, for example, search operating manuals and technical documentation for troubleshooting guidance. An identical historical fault case is not strictly required.

Pairio primarily uses approved internal company data, including technical documentation, historical fault and repair notifications, maintenance reports, and information from connected systems. It can also use content captured through voice, photos, or video.

The potential impact depends on each operation, use case, and starting point. Pairio can provide technical information and previous solutions faster, helping reduce repair time and unplanned downtime. In suitable use cases, MTTR can be reduced by up to 25%, filing effort by up to 80%, and onboarding time by up to 90%. Actual results vary by site and operating environment.

A traditional CMMS primarily manages assets, maintenance schedules, and work orders. Pairio focuses on search, documentation, analysis, and making existing knowledge easier to use. Existing ERP, CMMS, and document management systems do not need to be replaced; Pairio connects relevant information and adds AI capabilities to established workflows.

Pairio can integrate with existing ERP, asset management, and document management environments, including systems such as SAP, IFS, IBM Maximo, Oracle EAM Cloud, Hexagon, SharePoint, Outlook, and Asset API.

The introduction starts with a clearly defined use case and a review of the available documents, systems, and data. Existing technical documentation can be enough to begin; an extensive fault history is not required. Relevant sources and any ERP, CMMS, or document management systems are then connected according to the IT environment and intended use case.

Pairio is designed to require minimal ongoing administration after implementation. Once integrations and access rights are set up, it becomes part of the existing maintenance workflow, with reports and knowledge captured automatically from day-to-day inputs.