Today’s enterprises collect large amount of data every day. It is nearly impossible to analyze and identify important trends manually. Using the Managed Semantic Objects (MSOs) identified by Cloud Seer, Agora knows what kind of insight can be extracted from any data and what algorithms to use to get the best results. Agora’s machine learning module automatically applies multiple algorithms on the data and tests the results, eliminating the need for anyone to understand and program the system.
Bringing immediate value to leaders and teams, Platform Functions build an intelligent system that acquires, captures, and stores organization knowledge. Unique to App Orchid, these functions provide insight to business owners.
Core Services use Artificial Intelligence and National Language Processing to do the heavy lifting across the platform. These services parse and filter through the data, identifying patterns and trends that inform the answers that address enterprise challenges.
At the root of the platform lies the Foundational Services. These are the technical components that support configurations and programming and create the underlying framework of the App Orchid platform.
Agora comes with a pre-built repository of Ontology for different domains that helps in the automatic parsing and understanding of an enterprise’s data. This domain can be expanded for each enterprise as needed and also learned from unstructured data. This provides the platform with the capability to learn and adapt as new analytical processing is requested.
Agora has built in NLP capability that can process data in an unstructured natural language format. This is used to understand user’s questions, create ontology from documents, extract entities from text, search for similar or related information, etc.
When presenting information to the user, Agora will weigh the results to see what the most important and pertinent information is and display them in the order of relevance. The recommendation engine can take in feedback from the user and other users in the enterprise to start suggesting apps that are most relevant based on the usage.
Each organization has its own set of terms which needs to be understood in order to be able to answer questions. Agora can consume documents in and unstructured format and extract the required terminology and its definition that is stored as part of the ontology and in most cases mapped to an existing ontology.
Prediction, anomaly detection, entity identification, recommendation are standard ML models that are built into Agora, that can take data from a new source and start producing results immediately. All these models can be fine-tuned until the desired output confidence is achieved.
Agora remembers the context of the questions asked in order to support a conversation with the user. The co-referencing module keeps track of the subject of the question so that a follow-up question can be answered without providing all the details.
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