Automation
AI that knows your process and can take the next step.
An agent can check information, prepare a reply and carry out an agreed action in a system. We design solutions based on your company's knowledge, with clearly defined permissions and a set point at which a case is handed over to a person.
Scope
From answering a question to completing a task
Give customers an answer when they need it
Questions about your offer, availability, terms of cooperation and the status of a case often come back every day. An AI agent can handle an agreed range of these conversations using your company's knowledge. It helps find the right information, asks for missing details and guides the customer to the next step. This way, visitors can get initial help even outside your team's working hours, in the channels covered by the implementation.
We design conversations around specific goals: explaining the offer, collecting an enquiry, preparing a booking or handing a complete case over to an employee. We also agree when the agent should stop answering on its own. Your team can receive a summary of the conversation together with the information provided by the customer. This lets the contact start on the right topic and reduces the need to ask basic questions again. The scope of the agent's knowledge and actions remains clearly defined.
Connect AI answers with actions in your systems
The greatest value comes when a conversation leads to the task that needs doing. The agent can check a status in the system, look up information about the offer, prepare a note in the CRM or create a ticket for the right team. We connect it to selected tools and agree which data it can access. This way, the answer can take the customer's situation into account, and the outcome of the conversation feeds into the rest of the process.
A similar solution can be used inside the company. Employees find a procedure faster, compare information from documents or prepare a draft reply to an enquiry. For actions that require a decision, the agent can stop at a proposal and ask for approval. You choose the level of autonomy appropriate for each task. Automation is meant to take repetitive work off people's hands while keeping control over what the system records and who it passes information to.
Start with a task whose results can be assessed
We begin an agent implementation with a specific use case and examples of the cases it is meant to handle. We review the knowledge sources, prepare scenarios and agree the criteria for a correct answer. The measure might be the time needed to prepare material, the share of questions handled correctly or the completeness of the tickets passed on. This way, it is clear from the start how to assess the solution's usefulness and what to improve before extending its responsibilities.
We also test difficult situations: missing information, conflicting data or a question outside the scope. AI can make mistakes, so we design the handover to a person and the way its work is supervised. A pilot shows the real questions users ask and lets us fill gaps in the knowledge. Only on that basis do we expand to further channels, integrations and tasks. You get a solution tailored to your company's process, with a defined goal and rules for further development.
Process
How we implement an agent for a specific task
Choosing the use case
We look for a repetitive task and agree on a measure of usefulness.
Knowledge and access
We organise the sources, the scope of data and the agent's permissions.
Operating scenarios
We define the tools, the limits and the handover to a person.
Testing on examples
We check the answers, the mistakes and the behaviour in unusual situations.
Pilot and development
We launch a limited scope, assess quality and develop the solution.
FAQ
Questions before you start a project
Don't see your question? Write to us - we'll be happy to answer.
How is an agent different from an ordinary chatbot?
A chatbot often stops at giving an answer. An agent can also use tools and carry out steps in a process, for example read a status or prepare an entry in the CRM.
Can the agent make mistakes?
Yes. That is why the project includes testing, limits and verification rules. For actions with greater consequences, approval by an employee can be required.
Does company data go to an external model?
That depends on the chosen architecture and provider. Before implementation we agree the scope of data shared, the terms on which it is processed and the requirements for the environment.
What else we can combine with this project
What question does your team hear ten times a day?
Describe a recurring case and show us where the information it needs is kept. We'll assess whether an AI agent is the right solution.
- Scope tailored to your goal
- Delivery split into stages
- Room for further development