What is Cobbai?
Cobbai offers a suite of AI agents specifically designed to revolutionize customer service interactions. It introduces three distinct agents: Front, Companion, and Analyst. The Front agent interacts directly with customers 24/7 via chat, email, and self-service portals, aiming to provide instant resolutions and boost sales conversions. The Companion agent assists human support teams by drafting responses, summarizing interactions, and translating languages, thereby reducing handling times and improving service consistency. The Analyst agent focuses on improving efficiency by intelligently routing requests, detecting contact reasons, identifying detractors, and surfacing product insights from customer feedback.
These AI agents can be deployed through Cobbai's own customizable chat interface (Cobbai Chat) on websites or internal platforms, or integrated seamlessly into existing help desk ecosystems. Cobbai emphasizes user control, allowing businesses to train their agents with specific instructions and resources, test their performance in a sandbox environment, define their operational scope, and continuously monitor their outcomes and support metrics for ongoing optimization and enhanced customer experiences.
These AI agents can be deployed through Cobbai's own customizable chat interface (Cobbai Chat) on websites or internal platforms, or integrated seamlessly into existing help desk ecosystems. Cobbai emphasizes user control, allowing businesses to train their agents with specific instructions and resources, test their performance in a sandbox environment, define their operational scope, and continuously monitor their outcomes and support metrics for ongoing optimization and enhanced customer experiences.
Features
- Front Agent: Customer-facing AI for instant resolutions and sales conversions.
- Companion Agent: AI assistance for human agents (drafting, summarizing, translating).
- Analyst Agent: AI for routing, qualification, sentiment analysis, and insights.
- Cobbai Chat: Customizable AI-powered chat interface for websites and internal platforms.
- Help Desk Integration: Connects AI agents with existing customer service tools.
- Agent Control Suite: Tools to coach, test, activate, and monitor AI agent performance.
- Multi-LLM Support: Compatibility with various Large Language Models (GPT-4o, Mistral Large).
- Retrieval-Augmented Generation (RAG): Utilizes knowledge bases and past answers for informed responses.
- Voice of Customer (VoC) Lab: Analyzes sentiment, trends, and feedback for insights.
Use Cases
- Automated Customer Support
- Sales Enablement through Support Interactions
- Improving Agent Productivity
- Real-time Ticket Routing and Qualification
- Customer Sentiment Analysis
- Product Roadmap Insights Generation
- Multilingual Customer Service Assistance
- Internal Employee Support via Chat
FAQs
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What is a request?
A request, also called a conversation, is a unique feedback message written by one of your customers. For instance, it may be a review posted on Trustpilot or a customer support ticket received on Zendesk. Our plans prices vary based on the number of conversations you want to send through Cobbai. -
How does the pricing work?
To access Cobbai you need to contribute to a monthly base fee that depends on the features you subscribe. Then we invoice you the genAI costs that depend directly on the genAI workflows that you use and the LLM selected. Finally, if you activate the Cobbai Control Center, we charge a control fee per request. Price = Base fee + genAI consumption costs + control fee. -
How much does genAI cost?
The cost of genAI consumption depends on the LLM you select, the number of LLM requests done & their lengths. By experience the cost per ticket is between 0,03-0,20€/ticket. Each LLM invoice on the number of token that are processed. Please reach out to our sales team to have the price/token benchmark. -
What is a project?
Cobbai is based on projects. For each project, you can select a set number of integrations and comments you want to use. Our clients often start a new project to implement a specific workflow in order to improve one or a couple of CX metrics. -
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an NLP model combining retrieval and generative components. The retrieval model selects relevant information from a knowledge base. The generation model then crafts coherent responses based on the retrieved information. RAG is effective in tasks like question-answering and dialogue systems. It bridges the strengths of both retrieval and generative approaches for improved performance.
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