Accenture Global Solutions Limited

Pankaj Shrikant Nikumb

Amritendu Majumdar

Joel Samuel Kore

Karthik Srinivasan

Parikshit Maniar

Ashutosh Tripathi

Kavita Bhatt

Suchit Vishnupant Bhatwadekar

Badrinath Bahadur

Soumin Nikhra

Vimal Venugopalan

Marin Grace Mercylawrence

Shridhar D Rajgopalan

Examples of a digital orchestration system are provided. The system may obtain orchestration data on a real-time basis. The system may identify a plurality of events for offering a plurality of user services across a plurality of user interaction stages. The system may identify a plurality of actions associated with each of the plurality of events. The system may create a recipe associated with each of the plurality of actions. The system may identify and implement the associated recipe. The system may create an event sequence for each of the plurality of user interaction stages. The system may create a user service sequence comprising the plurality of user services associated with the event sequence. The system may generate a user experience result based on the event sequence and the user service sequence.

Experience orchestration

A device may receive user input data identifying a canvas, API documents, or tagged assets for an application to be generated, a requirements document for the application, and asset data identifying reusable assets and components for the application. The device may process the user input data, the requirements document, and the asset data, with a first model, to extract entity data and intent classification data. The device may parse the API documents to generate structured data identifying API endpoints, a request API model, and a response API model. The device may process the structured data to generate an API layer. The device may process the canvas to identify UI objects and to map the UI objects to UI elements. The device may generate code for the application based on the asset data, the entity data, the intent classification data, the API layer, and the UI elements.

Utilizing natural language understanding and machine learning to generate an application

In some examples, application architecture generation may include ascertaining, for a project, an input that includes project information, component information, and target information, and parsing the project information to determine whether the project is an existing project or a new project. Application architecture generation may further include generating a component list from the component information, ascertaining components from the component list, and mapping each of the ascertained components to a corresponding target determined from the target information. Further, application architecture generation may include analyzing a dependency for each of the ascertained components relative to at least one other component of the ascertained components, and generating, based on the mapping and the analyzed dependency, an integrated output that includes an architecture for an application associated with the project.

Application architecture generation

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Pune, India

Pankaj Shrikant Nikumb