Tool Information
ChatGPT for Customer Support, known as Parabolic, is an AI tool designed to enhance customer support services. This AI assistant is aimed at improving the efficiency and accuracy of ticket resolutions, eliminating repetitive queries, and providing specialized responses. Integrated within existing ticketing softwares like Intercom, Zendesk, or Helpscout, it uses a 'human-in-the-loop' model where responses are verified by agents before they're sent, allowing it to learn and improve over time. With just one match in past conversations or knowledge base, Parabolic can suggest responses, even addressing rarely asked queries. It's capable of navigating multiple back-and-forths as conversations progress. Another distinct feature is its ability to append relevant upsells or cross-sells based on the customer's issue, turning customer support into a potential sales channel. The 'Auto-Categorize' feature is designed to automatically tag and direct incoming questions to the appropriate category like payments, memberships, etc. It's also designed to detect a user's intent, independent of specific keywords, and draft two types of responses: generalizable and customer-specific. Generalizable responses can be used for common queries, while customer-specific responses utilize information from your internal database for more personalized assistance.
F.A.Q (20)
Parabolic is an AI-powered customer support assistant. It is optimized to speed up ticket resolution and is designed to be integrated into existing ticketing software. Parabolic can identify user intent and auto-generate replies, along with managing multiple rounds of conversations, attaching upsells and cross-sells based on customer's issues, and automatically categorize and index responses.
Parabolic integrates with existing ticketing software such as Intercom, Zendesk, and Helpscout. This integration enables it to embed within these platforms, providing seamless support without any need to switch to different tools or platforms.
Parabolic adopts a 'human-in-the-loop' model, allowing responses to be verified by agents before they are sent out. Every time an agent rejects a draft, Parabolic learns from these interactions and enhances its capability to improve with time, developing more accurate and sophisticated responses.
Parabolic's ability to detect a user's intent means it is designed to understand the underlying purpose or goal of a user's communication, regardless of the specific keywords used. This allows it to generate accurate responses that effectively address a user’s specific needs or concerns.
Parabolic auto-drafts responses by detecting user intent and then generating two kinds of replies: generalizable and customer-specific. Generalizable responses can be used for commonly occurring queries, while customer-specific responses make use of details from the internal database for a more tailored approach.
Yes, Parabolic is capable of managing multiple back-and-forths in a conversation. It can progressively navigate through ongoing dialogues and adjust its responses as the conversation evolves.
Parabolic uses upselling and cross-selling by detecting customer's issues and appending relevant upsells or cross-sells. This converts the customer support channel into a potential sales opportunity.
Parabolic's auto-categorize feature is designed to automatically tag and direct incoming questions into appropriate categories. For instance, the feature can classify questions related to payments, memberships, etc., making it easier to manage and respond to the queries.
The 'human-in-the-loop' model in Parabolic means that the response generated by the AI is verified by human agents before it is sent out. The model allows Parabolic to learn from the actions of human agents and continually improve its capacity to draft applicable responses.
Parabolic indexes responses by auto-indexing any response that you've ever messaged to a customer. Through this, it creates an extensive, reusable database for response management which enhances its capability to provide accurate replies swiftly.
Yes, Parabolic is capable of answering rarely asked questions. It only needs one match in past conversations or the knowledge base to suggest an answer to a question, no matter how infrequent it is.
Generalizable responses in Parabolic are those that can be used for common questions, while customer-specific responses are personalized responses that use data from the internal database to provide a more custom-tailored assistance.
Parabolic uses your company's internal database to draft customer-specific responses. This can include information like order details, account status, or any other client-specific data that can be used to personalize the responses.
Parabolic is committed to privacy and data protection, adopting a stringent privacy policy to ensure that all data is handled responsibly and securely.
Parabolic's terms of service highlight the rules and guidelines that users must agree to follow to use the service. It provides a detailed explanation of legal rights and responsibilities, ensuring usage transparency and accountability.
Parabolic's pricing structure is provided on their website and it's designed to deliver optimal value and affordability.
Parabolic increases response efficiency by integrating seamlessly with existing ticketing software, learning from previous interactions to improve response accuracy, auto-generating generalizable and customer-specific responses, and navigating through multiple rounds of conversation with the user.
Yes, Parabolic can be integrated with Intercom, Zendesk, and Helpscout. This ensures that the AI-powered customer support is available right within the platforms that companies are already using.
Yes, Parabolic is backed by Y-Combinator, a renowned startup accelerator known for nourishing innovative tech startups.
Parabolic handles different categories of customer queries, such as payments or memberships, by using its auto-categorize feature. It automatically tags and directs incoming questions to appropriate categories.
Pros and Cons
Pros
- Accelerates ticket resolution
- Embedded within existing software
- Trained on user's data
- Detects user's intent
- Auto-drafts responses
- Handles multiple exchanges
- Adds upsells/cross-sells
- Auto-categorizes incoming questions
- Auto-indexes sent responses
- Answers rarely asked questions
- Uses human-in-the-loop model
- Improves over time
- Integrated with Intercom
- Integrated with Zendesk
- Integrated with HelpScout
- Converts support to sales
- Auto-tags incoming questions
- Drafts two response types
- Handles generalizable/customer-specific responses
- Uses internal database data
- Privacy policy available
Cons
- Limited to existing ticketing software
- Agent verification slows responses
- Dependent on past conversation data
- Possibility of inappropriate upsell/cross-sell
- Automated categorization errors
- Limited to specific databases integration
- Demands extensive user-specific data
- Potential privacy issues
- Pricing not specified
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