Abstract: 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.

Abstract: 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.

Abstract: A computing platform is configured to (i) receive a request to identify schedule-item data objects within a construction management software application that are related to a submittal data object, (ii) identify one or more schedule-item data objects related to the submittal data object, (iii) generate a respective dynamic link between the submittal data object and each of the schedule-item data objects, each schedule-item data object including a respective start date, (iv) receive an indication of a change to a due date of the submittal data object, (v) determine a respective change to the start date of each of the schedule-item data objects based on the respective dynamic link between the submittal data object and the schedule-item data object and the change to the due date of the submittal data object, and (vi) update the start date of each of the schedule-item data objects in accordance with the respective change.

Abstract: An example computing platform is configured to: (i) receive an incoming request to identify relationships between data objects within a construction management software application; (ii) identify an AI agent to handle the incoming request; (iii) generate, via the AI agent, a search query for identifying a candidate set of data objects related to a given data object; (iv) perform a search with the search query to identify the candidate set; (v) generate a prompt comprising a request to determine which of the candidate set are related to the given data object; (vi) pass the prompt to a generative AI model; (vii) receive, from the generative AI model, a prediction of which of the candidate set are related to the given data object; (viii) based on the prediction, determine a final set of data objects related to the given data object; and (ix) send a response identifying the final set.

Abstract: A computing system is configured to: (i) cause a user to be presented with a unified user interface for creating data objects of multiple different types, (ii) cause the unified user interface to include a first set of input-control elements for a first set of data fields corresponding to a first type of data object, (iii) receive an indication that the selection of the type of data object to create has been changed from the first type of data object to a second type, (iv) cause the unified user interface to include a second set of input-control elements a second set of data fields corresponding to the second type, where a subset of the second set of data fields are pre-populated with a subset of the first set of values that were previously input based on a mapping between the first and second sets of data fields.

Abstract: A computing platform is configured to (i) receive data objects related to a construction project, (ii) add the data objects to a construction knowledge graph as nodes that are connected to other nodes representing other data objects, (iii) determine, via a machine-learning model trained using historic construction project data, a first risk score for a first data object, (iv) determine, via the machine-learning model, a second risk score for a second data object, where the second risk score is based on (a) the first risk score and (b) a degree of separation between the first data object and the second data object in the construction knowledge graph, (v) based on the second risk score, automatically generate a suggested action to be taken with respect to the first data object, and (vi) cause an indication of the suggested action to be displayed at a client station of a user associated with the construction project.

Abstract: A computing platform is configured to (i) load source data comprising (a) a set of log entries of a construction project and (b) a schedule of the construction project, (ii) preprocess the source data, (iii) provide the preprocessed source data as input to a large-language model (LLM) that is configured to generate insights related to progress tracking and thereby determine one or more insights related to progress tracking for the construction project, and (iv) cause the one or more insights to be presented to one or more individuals involved in the construction project.

Abstract: A computing system is configured to: (i) generating a first prompt for input to a generative AI model, wherein the first prompt comprises input data and a request to determine a type of log to be generated, (ii) inputting the first prompt to the generative AI model, causing the model to output an indication of a type of log to be generated, (iii) based on the indication, obtaining a template for the type of log to be generated, (iv) generating a second prompt for input to a generative AI model, wherein the second prompt comprises the template for the type of log to be generated, the input data, and a request to generate a construction activity log of the indicated type, and (v) inputting the second prompt to the generative AI model thereby causing the generative AI model to generate a construction activity log of the indicated type.

Abstract: An example computing platform is configured to: (i) receive, from a client device associated with a given user, a request to access a given GUI view of a construction management software application; (ii) obtain a set of baseline GUI data for the given GUI view; (iii) obtain a set of feature data for use in personalizing the given GUI view for the given user, (iv) obtain a set of payload data including content to be made available to the given user via the given GUI view; (v) generate a personalized version of the given GUI view based on the set of baseline GUI data, the set of feature data, and the set of payload data; and (vi) cause the personalized version of the given GUI view to be presented to the given user.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computing platform configured to perform functionality that involves (i) receiving an indication of a request for media content related to a given construction project, (ii) utilizing a planner agent to generate a sequence of tasks to be performed by other agents to generate the requested media content, (iii) utilizing a knowledge agent to perform a first subset of the sequence of tasks to obtain a set of project data for use in generating the requested media content, (iv) utilizing a production agent to perform a second subset of the sequence of tasks to generate the requested media content, and (v) causing the generated media content to be presented via a client device.

Abstract: A computing platform is configured to (i) load source data comprising (a) a set of log entries of a construction project and (b) a schedule of the construction project, (ii) preprocess the source data, (iii) provide the preprocessed source data as input to a large-language model (LLM) that is configured to generate insights related to progress tracking and thereby determine one or more insights related to progress tracking for the construction project, and (iv) cause the one or more insights to be presented to one or more individuals involved in the construction project.

Abstract: A computing platform is configured to: (i) train a machine-learning model by carrying out a machine learning process on a training data set that includes that includes construction-based data objects including indications of a plurality of building elements and indications of respective logical relationships between pairs of the building elements, (ii) receive a request to generate a building elements graph for a given construction project, (iii) input construction project data associated with the given construction project into the machine-learning model, thereby (a) identifying building elements of the given construction project, (b) determining a set of respective physical relationships between pairs of the building elements and a given set of respective logical relationships between pairs of the building elements, and (c) based on the given set of respective physical relationships and the given set of respective logical relationships, generating a building elements graph for the given construction project

Abstract: 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.

Abstract: A computing platform is configured to: (i) train a machine-learning model by carrying out a machine learning process on a training data set that includes historical construction-based data objects, (ii) receive a request to generate a construction-based data object associated with an ongoing construction project, (iii) receive data values for data fields of the construction-based data object, (iv) input one or more data values for data fields of the construction-based data object into the machine-learning model, as the input data values, and thereby generate an updated data value for the data fields, (v) cause a client device to present a visual interface, the visual interface usable for viewing an indication of the construction-based data object and an indication of the updated data value, and (vi) update the data fields of the construction-based data object, based on the updated data value.

Abstract: Techniques for dynamic pull planning involve (i) determining an update to a master schedule for a construction project that comprises tasks having respective scheduled start dates, (ii) executing a machine learning model that has been trained with historical construction project schedule data and thereby identifying candidate tasks each available for commencement earlier than its scheduled start date, (iii) causing 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, (iv) receiving user input indicating selection of a given task that is to be commenced earlier than its scheduled start date, (v) and causing transmission of a notification to a party responsible for completing the given task indicating that the given task has been nominated for earlier commencement and requesting approval for the earlier commencement.

Abstract: A computing platform is configured to: (i) train a large language model (LLM) by carrying out a first machine learning process on a first training data set that includes first construction-based data associated with one or more of a user, a plurality of reference construction projects, a construction-based application of the computing platform, or combinations thereof, (ii) receive a request to generate a construction activity summary, which includes a context-based prompt, (iii) generate the construction activity summary by inputting the request into the LLM, the construction activity summary including a contextual response, and (iv) retrain the LLM by carrying out a second machine learning process on a second training data set that includes the first training data set and one or more of the context-based prompt, the construction project data, an evaluation, the contextual response, the construction activity summary, a given timeframe, the request, input, or combinations thereof.

Abstract: 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.