Solve Tickets Within Seconds with AI Copilot for Zendesk

AI mostly contributes to customer support by automating the routine and assisting human agents, as the Zendesk AI chatbot does, in particular.  Different tools offer different levels of automation. Here are a few examples of the most popular AI features embedded into the tools for customer support:

• sentiment analysis – emotions assessment for obtaining subjective information from customer interactions;

• intention analysis – categorization of the underlying intention behind a user's text, such as requesting information, expressing a complaint, or making a purchase;

• automated ticket routing – assignment of incoming requests to the most appropriate agent or department based on factors like topic, urgency, customer history, and agent expertise;

• personalization – support interaction adjustments based on a customer's history, preferences, and behavior.

There are several AI-powered tools for enhancing customer experience, including the most popular chatbots and virtual assistants. They contribute to workflow automation or provide assistance to human agents; however, not all of them are based on AI. Some of the chatbots are built on simpler approaches, such as straightforward algorithms, If-Else conditions, and keyword search. As for AI technologies, solutions for customer support are based on such AI-related technologies as:

• language models;

• machine learning;

• reinforcement learning.

The ultimate goal of Al implementation in customer support is to provide quick, relevant, and personalized answers to customers' requests. Answers can be provided either by an Al assistant or a human agent with an Al assistant's support.

CoSupport™ Agent is a solution that provides the benefits of the synergy between response assisted automation by AI and a human in the loop. The solution integrates with the Zendesk AI chatbot and offers human agents response drafts containing relevant information, precise and context-aware. The solution works as a browser extension; it naturally integrates into the workflow and speeds up performance.

One particular model in our architecture ensures data privacy: it cleans data from personal information before data becomes the source for our models' learning for further integration with Zendesk. The system obtains all the context it needs while data is anonymized. Data security is guaranteed by using a dedicated server for data storage.

CoSupport™ Agent works as a browser extension. After the request is received, an agent gets the response draft in the same window. An agent chooses the best answer from two suggested items, edits the draft, or creates the response from scratch.

There are several advantages of such Zendesk chatbot integration:

• With AI-crafted replies and insights, your agents resolve tickets faster, increasing customer satisfaction and loyalty.

• Our custom model will train on your data, ensuring data security, superior quality, and trust.

• You get composed detailed suggestion replies to any customer request based on the latest data.

• Our text markup tool highlights crucial parts and visually structures meanings and intentions.

• Our AI learns with every interaction, constantly evolving and improving.

As a result, tickets are processed 30-80% faster. The response time decreases considerably.

To get access to the AI assistant’s functionality, you should take the following actions:

1. Data extraction for training models. Data is collected from the sources provided by a company. The primary source is Zendesk, and all information about customers it includes. Other sources are the knowledge base, documents describing products and services, policies, instructions, and guidelines for customer support agents. CoSupport™ Agent works with all types of textual information. We can also use call transcripts.

2. Data anonymization. All data goes through a separate model that anonymizes it. Such personal characteristics as a name, credit card info, location (up to 16 types of data) will never serve as data for training our models.

3. Model training. All materials are fed to models.

4. Solution launch. Access to the admins and end users (human agents) is provided.

How much time Zendesk chatbot integration takes depends heavily on the amount of data for model training and handling organizational issues. The process can take from a few hours to a few days.

Most of the data is taken from Zendesk. We use the following types of data derived from the CRM:

• tickets (we need 1000+ tickets for training);

• emails;

• product reviews;

• comments;

• call transcripts.

Additional data are as follows:

• SOPs;

• articles from the knowledge base;

• the content of internal and external websites, including FAQ;

• policies;

• step-by-step support instructions.

All documentation that your customer service agents use to handle their tasks is the relevant and workable data source for feeding the AI models. We work with all sorts of textual data in different formats, including PDF, Excel, Google Sheets, JSON, etc.

It’s hard to estimate Zendesk bot integration with AI because the amount of resources needed varies from project to project. However, one core issue can be stated definitely: the budget is based on the package subscription fee, unlike the budgets of using LLMs such as GPT, where you pay for tokens, and their amount can be unpredictable.