Autonomous Customer Service

It means a request is received, filtered, classified, resolved where possible and routed to the right workflow when it cannot be — without human intervention. AI bots handle chat, WhatsApp and self-service; written-channel agents monitor email, social media, reviews and marketplaces and turn them into tickets. Workflows take over the rest.

A chatbot runs the conversation and stops there. An autonomous setup covers what comes after it as well: it categorizes the ticket, pulls order and customer data from integrated systems, selects the corrective path according to policy checks, starts the SLA clock and triggers escalation when needed. The agent who takes over sees the full context.

Auto-Responder handles this. As the ticket moves through the workflow every development is written to the ticket history; Auto-Responder analyzes that history and the current status to decide when the customer should be updated, and informs them automatically on their preferred channel at those points.

Automatic resolution requires access to real data: ERP/OMS for orders and stock, CRM for the customer, SSO/LDAP for identity, channel services for communication. These connections are established bi-directionally through REST APIs and webhooks; the data then becomes usable inside both the bots and the workflows.

Yes. Guided Bot scenarios, the category tree, business rules, forms and workflows are configured with visual editors, without writing code. What the bot asks in which category, how far it goes and at which point it hands over to an agent are all determined by scenarios the business team defines. No code is written except for integrations.

Yes. Churn Analysis produces a graded satisfaction score by taking into account the sentiment data across all of a customer's tickets. Customers at risk of dissatisfaction or likely to be lost are spotted early through this score, and automated actions can be started for them. AI Analyzer →

We are not tied to a single model; proven large language models are monitored and the leading model of the period is used, and switching requires no project on your side. The rule-based Guided Bot and the LLM Bot work together: the flow handles the predictable part, and the LLM takes over when the conversation goes outside the scenario. More on our AI approach →

Yes, there are three options. By default the leading model runs as a managed service. If on-premise is preferred, a model that supports on-premise deployment is run on infrastructure you determine, under your organization's control. If data must not leave the country, it is stored and processed locally, and training is carried out on our own servers in that region without requiring hardware from you. Security and compliance →

Tickets resolved by the bot, transferred to an agent and routed into a workflow are reported separately; you can see in which categories automation holds and where it stalls. Sentiment analysis, ticket forecasting and category summaries show the areas where you can extend autonomy. Reports are followed in built-in dashboards and can also be exported to external BI tools. AI Analyzer →

Yes. Every ticket's history records who opened it, how long it waited at each step, who wrote what, and which type, category and sentiment prediction the AI made with which confidence score; whether the agent corrected that prediction is visible too. An auditor can reconstruct the entire process from a single ticket; this trail feeds quality management, compliance and agent training together. Maturity test →

Bots and agents do not work with general internet knowledge; they work with the organization's own documents, knowledge base, past resolutions and live data pulled from integrated systems through APIs. Answers rest on those sources, and they are traceable and auditable; new or changed documents can be added after training as well. We have collected these principles in a written framework. Responsible AI Framework →