Our universal messaging hub lets you seamlessly communicate with users on all the major chat platforms. Wherever your users are - Mercury.ai easily connects your bot to all the relevant messaging channels and even maintaines identity and context across channels. So you can offer your users a true multichannel conversational experience.
One of the guiding principles behind Mercury's NLU components is that everything in a bot can be done conversationally. Visual elements like buttons support the conversation, but everything can also be achieved by talking about it. This makes a bot independent of the particular channel, so that it can, for example, immediately be ported from a messenger to a purely speech-based channel, where visual elements like buttons are not available.
The Mercury.ai Dialogue Engine and Conversation Creator introduce dynamic, maintainable and user-friendly dialogue management. This is where the special power of the Mercury.ai platform comes to play. Typically today chatbot conversations are created by either of two methods: Conversation trees or „intents as action triggers“. By crafting conversation trees we lay out the possible routes of the dialogue. This will become overwhelmingly complex as we go into the most intricate details of dialogue. Think of creating a consistent capacity for semantic reference resolution in conversation trees. When user intents are directly mapped as triggers to bot reactions we will also run into unmaintainable complexity. Here it‘s a problem of controlling the bot behavior in a maze of unorganized triggers.
Find conversation logs across all channels in one central inbox. Together with each conversation you'll find the user's profile information - showing their preferences, subscriptions and any other profile data that you sync with your CRM.
See in real time how your customers are using the bot and identify improvement potential and failed interpretations. Differentiate your analysis by channels and content and see the most important KPIs at a glance.
Create and test variations of your bot and distribute them to a segment of your users. This lets you easily validate new user stories and conversational designs as well as data integrations or campaigns.
Building and running successful enterprise chatbots requires collaboration across stakeholders and teams within an enterprise and a clear definition on roles. Collaborative bot configuration across teams and departments is supported by permission schemes that reflect the different roles of team members, life-cycle management and staging that meets the demand of corporate release processes and goes so far to invite partner agencies to collaborate on the various projects of a license holder.