Personalize Each Interaction
and provide a conversational experience that feels human
and provide a conversational experience that feels human
Extraordinary IVA development experience with smart co-piloting, dynamic conversations, and advanced analytics.
Effective context management enables virtual assistants to interact with users more quickly and intuitively; it means less robotic and scripted. Contextual data helps assistants complete tasks faster and creates a more natural, human-like back and forth conversations. Personalize experiences to gain higher user satisfaction.
The platform allows you to capture and reuse contextual data for a large variety of scenarios to create more complex use cases and redefine the user experience.
Manage data and contextual details at the framework level – data that can come from user inputs, API responses, sessions, and more – with little coding required. It enables you to create context-aware assistants to follow conversation history, harvest and harness information from third-party systems, and accurately predict and populate appropriate tasks or responses.
When you create tasks, you can access session variables provided by the XO Platform or custom variables you define and the context that defines the variable’s scope.
Store and retrieve data for various use cases, such as personalized greetings, determining where prior conversations left off, and pre-filling order details or recommendations.
You determine how long these variables are stored and accessible by your assistants – so you can create more intuitive conversations with users on your terms.
Humans tend to switch back and forth between intents. IVAs developed on the Kore.ai XO Platform handle these situations (digressions) efficiently.
The platform also offers provisions to configure the conditions to enable or disable context switches; also add conditional exceptions between tasks with the ability to pass contextual data between them. The platform handles virtually all complex and diverse content-switching scenarios effectively.
Create virtual assistants that remember user inputs and answers – and automatically modify their behavior based on sentiment and context.
Connect to your backend business systems to pull and store the necessary data– otherwise often static, contained within a single location, and rarely remembered in context. The platform allows you to create dynamic conversational experiences that can customize, categorize, and apply contextual information as you see fit.
What: Individual user information and preferences can be stored and used in varying tasks. This information can be shared with all assistants during user interaction.
Why: Transactions and interactions are made shorter and easier, as users no longer have to provide the same basic information repeatedly.
Example: Information like a user’s home address, payment information, airline seat, or baggage preference can be stored and remembered for future interactions.
What: Information representing company-wide rules and standards for all users and assistants.
Why: It allows developers to ensure that assistants keep company requirements in context and enforce them as needed.
Example: A company travel or expense policy could be applied that overrides an employee’s preferences when using a travel assistant to book a flight or hotel.
What: Information shared by the user that the assistant must remember during a session.
Why: Avoids users from repeating relevant information to complete multiple tasks in a single session.
Example: A bank customer says, “how much do I have in checking?” and then asks to “transfer $500 to savings.” The assistant could recognize “checking” as session context and thus make the transfer without asking the user, “from which account?”.
What: Information shared by the user that the assistant must remember during a session.
Why: Keeps the user from having to repeat information that’s relevant for completing multiple tasks in a single session.
Example: A bank customer says, “how much do I have in checking?” and then asks to “transfer $500 to savings.” The assistant could recognize “checking” as session context and thus make the transfer without asking the user, “from which account?”.
Manipulate API responses, promote additional data to the user context, and pull data from the user context with support for custom code logic. This allows you to seamlessly extend the existing functionality of the platform and create advanced, custom conversational experiences that are driven by virtually any form of context.
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