Once the use-case and the value are identified, the value calculated and validated, the next step is to deep dive into implementing a language model.
A language model is a collection of intents and entities that are fundamental components in the structure of a VA. Identifying the right set of intents and entities is important as it determines the quality of the VA going forward. Creating intents can become a complex task involving AI specialists. In an idealistic scenario, intents are identified by doing text mining by analyzing call transcription of historic data. One needs to use more than one data resources, either internally or from an external source, to identify all the possible intents and entities. One also needs to take into account synonyms, shorthands, and slang (for voice assistant) while designing the intents and entities. A typical customer support VA has in the range of 20 to up to 50 intents or even more.
The above-mentioned approach looks time-taking and daunting, demanding new skill sets and resources. This can be fast-tracked by making use of pre-defined skills provided by players like Smartbots that has Industry-specific and use-case specific skill librarirs. Making use of pre-built and tested skills not only reduces the time to deploy but also ensures the highest quality.