Phone calls,
emails,
messages...

Most inquiries in customer service are routine. Automate repetitive tasks to free up your team for complex cases.
Our goal is to assist people.

Which channel interests you?

“Two-thirds of millennials expect real-time customer service, for example, and three-quarters of all customers expect consistent cross-channel service experience. And with cost pressures rising at least as quickly as service expectations, the obvious response—adding more well-trained employees to deliver great customer service—isn’t a viable option.”

E-Mail Routing

The issue

Manual categorisation by employees takes an average of
35 seconds per email. Attempts to automate using keyword systems fail due to inaccurate categorisation. Topic-specific email adresses, such as electricity@cityutility.com, gas@cityutility.com, water@cityutility.com, create a communication barrier for your customers.

The solution

Automated categorisation based on Natural Language Understanding (NLU) technology. Our AI distinguishes finely and with high precision between any number of topics. The basis for decision-making is not individual keywords but the semantic context of the inquiry. Employees can focus on more valuable tasks, while customers can direct all their concerns to a single address.

E-Mail Hyperautomation

The issue

Manual categorisation by employees takes an average of
35 seconds per email. Attempts to automate using keyword systems fail due to inaccurate categorisation. Topic-specific email adresses, such as electricity@cityutility.com, gas@cityutility.com, water@cityutility.com, create a communication barrier for your customers.

The solution

Automated categorisation based on Natural Language Understanding (NLU) technology. Our AI distinguishes finely and with high precision between any number of topics. The basis for decision-making is not individual keywords but the semantic context of the inquiry. In addition, process-relevant data such as customer number, contract number, and meter number are automatically extracted from the text. These can be easily further processed using RPA (Robotic Process Automation) or any other automation solution. This allows employees to focus on more valuable tasks, while customers can direct all their concerns to a single address.

Generative AI

The issue

Responses to customer inquiries using predefined text blocks are impersonal, cumbersome to handle, and result in a poor customer experience. Technologies like GPT (LLMs) lack transparency in their operation (Black Box) and do not possess specific company knowledge, which is why they cannot generate relevant anwers on their own.

The solution

Concerns are recognised in a transparent manner, using Explainable AI. Based on this recognition, relevant information for answering the question is retrieved from the company’s knowledge base and made available to a Large Language Model (LLM). This allows the LLM to formulate a relevant, personalised response, which is then reviewed and sent by employees (‘Human in the Loop’).

“By 2025, customer service organizations that use AI-enabled knowledge automation
will achieve 90% first contact resolution, up from 50% in 2021.”
Predicts 2022: Customer Service and Support, Gartner
Designer

Call Routing

The issue

“Press 1 for…”, “Select 2 for…”, “Visit our website at…”. Who wants to hear that anymore? Even if the standard procedure allows for a maximum of six options and two levels, the customer experience remains unsatisfactory. Furthermore, the topics behind the IVR are so broad that effective skill routing is only possible in a very general way. Therefore, new employees need to acquire extensive knowledge during the onboarding phase before they can work effectively.

 

The solution

“Welcome! How may we assist you?” Thanks to the intelligence of deepassist, it’s possible to start the conversation with an open-ended question. Wether callers describe their concerns with just a single word or provide detailed explaination, deepassist is capable of understanding this accurately and can differentiate between 5, 500, or 5000 different inquiries. Routine inquiries can be routed to an automated process, while more complex questions are forwarded to the appropriately trained employees – exactly those who specialise in handling such inquiries. This gives you control over your call flow and optimised FCRR (First Contact Resolution Rate).

Voicebot

The issue

70% of customers still prefer using the telephone to contact companies. One challenge is ensuring constant phone availability, especially with a high volume of routine inquiries. These simple requests tie up resources that are needed for handling more complex customer concerns.

The solution

deepassist acts as the central intelligence for Voicebots and provides an efficient solution to the problem of continuous phone availability. The Voicebot is always ready to quickly and accurately handle routine inquiries such as meter readings or changes to installment plans. This not only increases accessibility for customers but also significantly relieves the customer service team.

Agent Augmentation

The issue

Agents are under increasing pressure to respond to diverse and complicated customer inquiries within a short time frame. Customers describe their problems in their own language, which often does not align with the specialised language used in the company’s internal documents. Agents must identify customers, categorise their concerns into the appropriate processes, know the necessary steps, and guide customers through them. This requires time, not only during the call itself but also in the weeks and months of prior training.

The solution

deepassist provides real-time support for agents on the phone. It assists them in executing necessary steps correctly and efficiently. During the conversation, the AI suggests relevant business processes, displays relevant information from knowledge databases, and automatically processes data such as license plates or similar details. this eliminates the need for agents to type, allowing them to fully focus on the conversation and problem-solving.

Virtual Assistant

The issue

Traditional chatbots require extensive training and often end up as rigid decision trees. They struggle to understand the true needs of users. Most users prefer a quick transfer to a human agent. As a result, this communication channel is rarely used by customers, preventing the expected relief for service teams.

The solution

deepassist enables dynamic and natural dialogues that are tailroed to the context of the respective organisation. Large Language Models (LLMs), such as GPT, can be used to further enhance the customer experience. Due to these technological capabilities, it is expected that customers will adapt their beavior in the next three to five years and increasingly utilise virtual assistants. This will ultimately provide the much-anticitpated relief for service teams.

Curious to know more?

We would be happy to advise you on your individual Use Case and the improvements you can achive with deepassist.

Ihr Ansprechpartner

Florian Silberbauer
Salesmanager