Pro-core Technologies Limited

Matt Man

Mohammad Soltani

Julian Clayton

Kevin McKee

Farid Mirahadi

Navin Kaminoulu

Taylor Wasser

Dan Park

William Gaines

Azadeh Yazdan Panah Gohar Rizi

An example computing platform is configured to: obtain a two-dimensional drawing of a portion of a construction project; perform an image processing analysis of the two-dimensional drawing to identify one or more location entities within the two-dimensional drawing; derive embeddings for each location entity in the two-dimensional drawing; based on the derived embeddings, determine relationships between the one or more location entities; and based on the determined relationships between the one or more location entities, generate a location entity data taxonomy that includes each identified location entity as a respective node that is related to at least one other location entity.

Computer systems and methods for identifying location entities and generating a location entity data taxonomy

An example computing platform is configured to: determine, via a first machine-learning model, a relationship between a data asset associated with a construction project and a first location entity associated with the construction project; update the data asset to include an indication of the relationship; receive an indication of a target status dimension and a request to generate a heat map for the construction project; determine, for the first location entity, a first value for the target status dimension based the relationship; generate data indicating the heat map, the heat map comprising a visual representation of the first location entity displayed in a first color along a color scale that represents the first value for the target status dimension; and transmit, to an end-user device, the data indicating the heat map and thereby cause the heat map to be displayed via the second end-user device.

Computing system and method for generating predictive heat maps for target status dimensions with data-driven recommendations

A computing platform is configured to: (i) receive, from a first client station, (a) data related to the first client station and (b) a first image associated with a target location; (ii) based on the received data related to the first client station, determine a location signature associated with the first image; (iii) determine that the location signature associated with the first image has a threshold level of similarity to a location signature associated with a second image that is associated with the target location; (iv) evaluate at least the first image to determine progression of a construction-related activity at the target location; (v) based on the evaluation of at least the first image, determine that the construction-related activity at the target location has progressed a threshold amount; and (vi) transmit, to a second client station, a communication related to progression of the construction-related activity.

Verification of progression of construction-related activity at given location

A computing system is configured to determine an update to a master schedule for a construction project that comprises a plurality of tasks, each task comprising a respective scheduled start date; execute a machine learning model that has been trained with historical construction project schedule data and thereby identify one or more tasks that is each a candidate for commencement earlier than its scheduled start date; cause a client station to display each identified task, its scheduled start date, a respective new start date, and an impact on the master schedule if the task is commenced on the respective new start date; receive user input indicating a selection of a given task that is to be commenced earlier than its scheduled start date; and cause a notification to be transmitted to a party responsible for completing the given task indicating that the given task has been nominated for earlier commencement and requests approval for earlier commencement.

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Thornhill, Canada

Matt Man