1. What restrictions do child flows have? Can the child's Flow's structure be edited or must it follow the Master?
The child flows must follow the structure of the master but are localized into the child solution’s language (training data provided in the target language, answers translated into the target language, etc). They might use different integrations (for instance, each region may have their own back-end systems such as ordering, CRM, databases, etc) and these variations can be easily parameterized to minimize the work needed for the children.
It is possible for a child to break its connection to the master if there is a requirement for a different process flow, but that decision is irreversible and the child will no longer be notified of or inherit changes in the master flow (for that flow only, not for other elements, where inheritance continues as before).
2. How does the robot accesses customer data?
Teneo access data through integrations with the appropriate backend systems generally using Java or Groovy.
Within Teneo Studio is a dedicated Integration Manager, separating the code components from the conversational components and allowing your programmers to create the appropriate integrations for use by non-technical conversational AI designers building the solution. You can create libraries of these integrations for reuse across multiple solutions and Artificial Solutions provide pre-built integrations for many common systems.
3. Do you have a library in pt br that can be downloaded to use on Teneo by default?
Yes, Artificial Solutions provide pre-built libraries for all the languages that we support including Brazilian Portuguese. Further note that these libraries can be extended by you to incorporate additional vocabulary you might have such as company-specific verbiage, product names, etc.
4. How well can Teneo do mixed language?
Supporting mixed language in the context of a chatbot is quite difficult due to the subtleties of language as well as the fact that one word may have different meanings in different languages. For example, in English a gift is a present and something desirable where as in German, Gift is poison. For that reason, chatbots are typically deployed to support a single language each. Teneo can detect when a user is using a language different than that of the solution and react accordingly.
As shown in the demo, this might include seamless transfer to a different chatbot which speaks that language, providing an alternative contact channel such as call center or live chat, or notifying the customer that their language isn’t currently supported and providing a list of those which are. Also of interest, this is all logged and can be reviewed to make future business decisions so that, for example, if 15% of users begin speaking an unsupported language, that might be targeted as the next chatbot solution language.
5. Isn't there a lot of work to create all those conversations/chains?
No, the goal of Teneo is to remove the complexity and effort from building conversational solutions. In a typical project, the first deployment of the bot can occur within 8 – 12 weeks of project start, allowing for rapid time-to-market. As an example, the “my bill is too high” flow seen in the demo too, only a couple hours to create (though there was refinement after that initial build). Further, as Teneo was created with the enterprise in mind, you do not need to rely on Artificial Solutions for development but can use your own non-technical subject matter experts to take complete control of development if you wish (though we do have a dedicated professional services team who can assist you ranging from providing occasional advice to completely building the solution for you). Artificial Solutions employ an iterative, agile methodology for solution creation, allowing you to quickly create and deploy usable bits of a solution without the need to fully deploy your entire project scope.
The particular focus of this webinar was deploying the same solution in multiple languages and the amount of effort saved with the master local functionality is tremendous, allowing you to build out your flow structures a single time and automatically recreate them (and update, optimize and add over time) in child solutions.
6. Do you support Arabic and their dialects?
Teneo does not currently support Arabic but it is a language on our roadmap and we are actively looking at providing support.
7. What languages does the solution support? Specifically Spanish, Portuguese, Mandarin, Cantonese?
Teneo currently supports 38 languages: Basque, Belarusian, Bosnian, Bulgarian, Catalan, Chinese (Mandarin), Croatian, Czech, Danish, Dutch, English, Esperanto, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Italian, Japanese, Latvian, Lithuanian, Macedonian, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovene, Spanish, Swedish, Turkish, Ukrainian. Learn More.
8. If we have one mega agent which is in English language and suppose we create multiple sub agents in different languages, does it automatically convert all the data in particular agent language for those sub agents?
As noted in the presentation, while it is possible to build a solution in one language (which needn’t be English) and integrate with machine translation to support additional languages, this will provide a sub-optimal, potentially even unusable, experience for users not using the language of the solution. We strongly recommend that the translation for child solutions be carried out by human translators who are able to understand the nuances of the language and accurately present the voice of your company in each of the target languages. That said, for more simple FAQ type solutions, machine translation might be a viable option.
9. Does multilingual solution imply any possible language or just the more developed and popular ones?
Teneo currently supports 38 languages with our unique hybrid model mixing a machine-learned classifier with our own linguistic conditions based on Teneo Language Resources (TLR), allowing for high accuracy intent recognition. Depending on the language, it would still be possible to use the classifier alone with currently unsupported languages and develop the TLR later. We can rapidly create TLRs to provide support for new languages in between 2 – 6 months, depending on the similarity to an already supported language.