Expert Systems MVP

Description

KTN wants to produce an automated self-service capability for our website that allows companies to access our expertise without necessarily talking to KTN personnel. We are looking to build this automated expertise through: Building a graph database of all our staff, events, offerings and companies in our CRM, etc [need: graph database] Building a machine-learning model based on the database [need: Machine learning model] We are looking to serve companies through: Automatically understanding their requests in natural language [need: Natural Language Processing (NLP)], Automatically guiding them through a replica of process that our experts run now in person, successively recommending the appropriate resources based on the companies' interaction with the system [need: ML model based on graph database, interacting with NLP]. This all will need to be integrated with our website, internal processes and existing data architecture [need: website, Gmail, CRM, etc integration], which is the primary form of interaction the companies have with us. Success factors Successful delivery will be defined as: Highly desirable UX and UI. Efficient and effective rank and recommendations for online enquiries. Automated data retrieval and cleansing. Efficient and effective interrogation and explainability. Handover and total control - updating data architecture/integration.

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