de Contextere

Madison extracts and curates industrial data and provides insights to industrial technicians.

The Madison insight engine by Contextere extracts and curates previously inaccessible enterprise data to provide actionable insights for industrial technicians. Madison combines automated data extraction and machine learning with an advanced virtual assistant to provide new insights for analysts and technical workers to empower better decisions and more effective execution.

Contextere is solving the problem of lost productivity, human error, and skilled workforce development on the ‘last tactical mile’, where skilled workers inspect, maintain, install, and operate complex equipment in the field or on the factory floor.

Global industries suffer lost productivity through equipment and workforce-related inefficiency due to a reliance on paper-based work instructions and largely manual validation of work performance for compliance assessment. While some companies attempt to improve the productivity of field workers by packaging all possible content and work instructions on tablets or laptops to carry the information to the field. Most of these mobile content solutions are ineffective because skilled workers prefer not to search through copious amounts of data to find the relevant information and will often default to memory or paper-based instructions. Neither of their existing solutions automatically provide the user with contextually relevant information when and where they need it most.

Madison addresses these issues by automatically extracting information, meaning, and semantics from structured, unstructured, and live industrial data and delivering actionable insights to the user via advanced virtual assistants. Our virtual assistants understand user location and role, and equipment location/status, to provide intelligent guidance and curated insights to industrial users. Madison uses a unique combination of natural language processing and neural networks to extract meaning from industrial enterprise data and determine the appropriate contextually relevant micro-guidance or insights.

This combination of data extraction, machine learning, and natural language processing increases situational awareness, builds semantic understanding of dynamic data environments, reduces cognitive overload, and enables peer-to-peer knowledge sharing. Madison reduces the time required to install, inspect, repair, and maintain complex equipment, increases the rate of first-time fixes, reduces rework and waste, and minimizes return field visits. 

Visão geral