// AI Agents
AI agents for your company.
Consulting and implementation from one team.
An AI agent plans its own steps, uses tools such as your CRM, databases, or email, makes decisions within clear rules, and executes multi-step workflows. We work out with you which process is a good fit, then build the agent for production use, GDPR-compliant and on your servers.
// Comparison
Chatbot, copilot
and AI agent compared.
Chatbot / Copilot
AI Agent
Technically, an AI agent usually follows the ReAct pattern: it reasons about the task, acts, observes the result, and repeats the loop if needed. Orchestrated with LangChain or LangGraph, the agent breaks complex tasks into subtasks and works through them in order.
// Use Cases
AI agents
across your business units.
Customer Service Agent
Handles tickets around the clock, routes requests to the right team, and draws on your knowledge base. The agent resolves simple level-1 requests itself and hands everything else to your staff. Connects to Zendesk, Freshdesk, or Intercom.
Marketing Agent
Drafts content, manages campaigns, evaluates A/B tests, and prepares performance reports. Learn more on our Agentic Marketing page.
Sales Agent
Qualifies leads, keeps the CRM up to date, sends follow-ups, and schedules meetings. The agent scores leads, enriches data, and prioritizes your sales pipeline.
Operations Agent
Automates processes, monitors systems, detects anomalies, and produces reports. Runs on n8n workflows on your servers.
Research Agent
Supports market analysis, competitor monitoring, and trend tracking. The agent searches sources, extracts and consolidates data, and delivers structured results.
Code Agent
Supports development teams with recurring work: code reviews, automated tests, deployment pipelines, and documentation.
// Architecture
How we build
AI Agents.
LangChain, LangGraph, n8n
APIs, DBs, Web, Mail, Slack
Docker, EU Servers, GDPR
Use case and scope
Which tasks should the agent take on, which tools does it need, and what does the workflow look like? We define the scope together with you. More about our consulting approach.
Architecture and technology
We select the language model, orchestration framework, tool integrations, and memory strategy. We use open source where possible and commercial APIs where they are needed.
Development and testing
We develop iteratively: agent logic, prompt engineering, tool connections, and guardrails. Testing uses real data and edge cases.
Go-live and monitoring
The agent goes live on your infrastructure with monitoring, logging, and alerting. It works on its own while you keep control.
Expansion and optimization
We then connect further tools, add agents, or build a multi-agent system, and optimize based on real performance data. On request we run the system for you from 2,500 EUR/month.
// FAQ
Frequently asked questions
about AI agents.
What is the difference between a chatbot and an AI agent?
A chatbot responds to inputs with predefined answers. An AI agent plans autonomously, uses tools (APIs, databases, web), makes decisions, and executes multi-step workflows. For example, it analyzes a customer inquiry, looks up data in the CRM, works out a solution, and sends a personalized response.
What tasks can an AI agent handle?
Recurring, rule-based, or data-intensive tasks: customer service (tickets around the clock), marketing (content, campaigns), sales (lead scoring, CRM), operations (monitoring, reporting), and research (market analysis, data extraction).
How long does AI agent development take?
Simple agents (FAQ bot with knowledge base): 1-2 weeks. Complex multi-agent systems: 4-8 weeks. We work iteratively, and a first working agent is often ready within a few days.
Are AI agents GDPR-compliant?
Yes, with the right architecture. We use EU hosting, self-hosted LLMs (Mistral, Llama via Ollama), data processing agreements, and data minimization. More about our GDPR-compliant AI automation.
Can an AI agent work with our existing systems?
Yes. AI agents integrate via APIs: CRM (HubSpot, Salesforce), ERP, databases, email, Slack, Google Workspace. Orchestration runs via n8n or LangChain.
What does an AI agent cost?
Simple agents from 5,000 EUR, complex multi-agent systems 15,000-40,000 EUR. Ongoing token costs are typically 50-500 EUR/month, depending on volume. We keep token usage lean and use open source where possible.
// Next step
Find the right
use case.
In a free 30-minute initial call, we work out which process suits your first AI agent and sketch the architecture. Our team has delivered 10 AI systems in production across 6 industries.
Book an Initial Call