An LLM-powered agent assistant embedded in every step of customer service. It understands the conversation, case context, and your policies—then suggests next actions and response drafts for agents, supervisors, and back office teams.
An LLM-powered agent assistant embedded in every step of customer service. It understands the conversation, case context, and your policies—then suggests next actions and response drafts for agents, supervisors, and back office teams.
How it worksSurfaces guidance instantly: likely causes, step‑by‑step procedures, policy nuances, and cautions. Suggests the right questions, links the right articles, and drafts replies or internal notes for review. Agents stay focused and informed without leaving the conversation.
How it worksGround it in your documents, knowledge base, product manuals, internal docs, past tickets, and CX learnings. Train it on similar cases so guidance stays consistent with how your business actually works. Patterns from similar cases surface when agents need them most.
How it worksLess searching, less second-guessing—more confident, uninterrupted service. Agents resolve faster with the right information at their fingertips. Policy and procedure guidance keeps responses consistent and compliant.
How it worksWe went through an adaptation process consisting of 2 parts. While we, as the business team, designed the category tree, interfaces, and processes needed for ticket management by leveraging the Next4biz team's experiences for each of our business channels, the technical teams integrated all our systems.
Next4biz has a structure that allows us to make the changes we want. Moreover, it's a local product. We decided to use Next4biz to be able to track our customers' history, facilitate communication between internal departments, and be able to stay with the customer until their ticket is resolved.
In the next phase, we brought the system's most efficient capabilities into our business processes and accelerated our resolution time. We now manage tickets more efficiently across channels.
The questions asked most often about the draft reply the Supervisor Bot offers the agent, the knowledge its suggestions draw on, and the fact that the final decision stays with the agent.
The Supervisor Bot is an LLM-based agent assistant embedded in every step of customer service. It understands the conversation, the ticket context and company policy; without the agent leaving the conversation, it brings possible causes, step-by-step procedures, policy nuances and warnings into view. It suggests the questions that should be asked, points to the relevant knowledge articles and drafts a reply or an internal note. The same support is offered to supervisors and back-office teams. How it works →
Suggestions are produced from your organization's own sources, not from general internet knowledge: the knowledge base, product manuals, internal documents, past tickets and what the customer experience team has learned. The assistant can be trained to capture patterns in similar cases, so the guidance fits how your business actually works. Because the sources belong to the organization, every suggestion is auditable. See the AI page for the language model used and the data policy. Our AI approach →
The final word always belongs to the agent. The Supervisor Bot prepares the reply or the internal note as a draft; the agent edits it, approves it or does not use it. This is a deliberate design choice: the assistant is not a bot talking to the customer but a guide standing behind the agent. It flags risky steps and checks that must not be skipped, but responsibility for the text that reaches the customer stays with a person. For categories that need automated replies on written channels, the AI Agent is a separate component. Written channels →
By turning the knowledge base from somewhere the agent has to search into an assistant standing next to them. Under pressure an agent does not search; they write what they know. Because the Supervisor Bot brings the correct procedure and policy rule into view at that moment, answers stay consistent and compliant regardless of who is answering. Learning from past resolutions, the assistant carries experienced agents' accumulated knowledge into organizational memory, and new agents become competent faster. You can measure where you stand with the complaint management maturity test. Maturity test →
No; alongside the front-line agent, the Supervisor Bot supports supervisors and back-office teams in the same way: the draft reply and the policy guidance arrive with the same context in every role. It also works in the call center: it recognizes the caller as the phone rings and surfaces the relevant scenario during the conversation; the detail is on the omnichannel page. So no matter which channel the ticket arrives from, the same policy guidance is used at every step of the resolution. Call center →
See our presentation and demos in a meeting with our product consultants, and let’s evaluate our autonomous customer service architecture together on your own channels and processes.
During the meeting, you can review with our experts: