Abstract: AI-based orchestration. In an embodiment, a recommendation engine is applied to a data pipeline representing company accounts. Engagement metric(s) are calculated based on activity data associated with the company accounts, and predictive model(s) are applied to the activity data and/or firmographic data associated with the company accounts to generate predictive output. A tactic recommendation model is applied to orchestration features, comprising the engagement metric(s) and predictive output, to generate recommended tactic(s). In addition, a contact recommendation model is applied to contact data to generate recommended contact(s). The recommended tactic(s) are combined with the recommend contact(s) to generate an orchestration, comprising recommended action(s), to be executed.

Abstract: Artificial intelligence for keyword recommendations. In an embodiment, raw keyword data are received. The raw keyword data comprise keyword activity records that each comprises a uniform resource locator (URL) for an online resource and metadata for the online resource. Arrays of keywords are extracted from the keyword activity records, with each array of keywords associated with the URL in the keyword activity record from which the array of keywords was extracted. User-specified keyword(s) are received, and a subset of the arrays of keywords that match at least one of the user-specified keyword(s) is identified. A training dataset is generated from the subset, and used to train a machine-learning model to output recommended keywords based on an input keyword.