80547Deep learning solution for improved conversational data analysis for distributed customer facing teamsClosedCollaborative R&DInnovate UKRegardless of sector or size of operation, significant volumes of conversational data are generated daily through the interaction between a business and its customers as well as through internal communications. Despite the potential value of this data to improve its customer experience, operational activity and competitiveness, the ability to truly analyse the content of complex interactions generated by the large volume of phone calls, emails, on-website chat generated by internal and external customer communication is far from optimal with a current reliance on manual interpretation to perform this activity. With significant variations in content, the ability to automatically extract both the meaning of a communication as well as the intent is a highly complex task which is not possible through current call analytics products or AI based solutions which on rely specific metadata, key phrases or sentiment analysis. Whilst this need has long been recognised, it has been significantly compounded as a result of the COVID pandemic as companies seek new opportunities to drive sales in order to survive with a disruption to the traditional ways of communicating amongst teams/customers and disparate working practices of sales teams particularly in an B2B environment. Despite the easing of easing of lockdown measures, many employees (and departments) continue to operate remotely with more tasks than ever being delivered by digital channels and phone calls as a replacement for physical interactions/in-person sales meetings, and with the majority of sectors facing challenging market conditions, the need to better understand a client's needs and more quickly respond to evolving market opportunities has become even more critical. Through the deployment of the first automated conversational intent recognition system capable of interpreting unstructured communications data from any form of communication and converting this from natural language to structured data, without the need for software expertise or additional coding input, Reinfer overcomes the limitations of both manual practice and emerging AI based solutions in this space. Whilst the ability to automate the interpretation of communication data has now been proven by Reinfer -- the ability to rapidly disseminate this across remote working sales and customer service teams has not. It is this capability that the proposed project seeks to deliver exploring the potential to be integrated into existing CRM systems and communication platforms e.g. slack and expanding functionality across wider communication channels. If successful, the solution has the ability to deliver significant socioeconomic impact across multiple sectors supporting increased sales, operational efficiencies and as a valuable support tool for disparate working teams. The solution can play a critical role in supporting both the recovery of UK business and future growth as well as help prepare for a potential 'new world' where virtual customer engagement increasingly replaces traditional face-to-face meetings.