Have AI Agents Built
Autonomous AI agents that handle tasks in customer service, sales and email triage on their own — GDPR-compliant, with clear guardrails and a first prototype in 2-4 weeks.
What autonomous AI agents deliver for your company
Not chatbot toys, but acting systems with measurable value — securely embedded into your processes.
Autonomous task completion
An AI agent plans, decides and acts independently across multiple steps — instead of just replying. It calls tools, checks results and works through tasks on its own all the way to the goal.
Tool use & system integration
Agents access your systems in a controlled way: CRM, ERP, ticketing system, calendar, email and databases. Through defined interfaces they carry out real operations, not just text output.
GDPR-compliant guardrails
Clear permissions, approval steps for critical actions, logging and EU hosting. Sensitive data stays under your control — with a data processing agreement and deletion concept.
Prototype in 2-4 weeks
Instead of months-long projects, we deliver a working prototype for a clearly defined use case in 2-4 weeks — measurable, assessable and without flying blind into a large budget.
Relief for your team
Agents take over recurring routine work such as request classification, research and data maintenance. Your employees focus on decisions, advice and complex cases.
Scalable & measurable
A proven agent is rolled out to further processes and departments. Processing time, automation rate and error rate make the ROI transparent from the start.
How we build your AI agents
From scoping the use case through frameworks and GDPR guardrails to productive operation.
Rule-based vs. AI vs. agentic
Rule-based bots follow rigid if-then paths, classic AI answers individual requests. Agentic AI plans intermediate steps, uses tools and adapts dynamically. We choose the right level — not every process needs an agent.
Use case: customer service
A service agent understands requests, searches the knowledge base and CRM, creates quotes, opens tickets and escalates to a human when needed. Around the clock, multilingual and with a complete history.
Use case: sales
Agents qualify leads, research prospects, personalize outreach, maintain the CRM and prepare follow-ups. Your sales team receives pre-filtered, decision-ready opportunities.
Use case: email triage
The inbox is automatically classified, prioritized and assigned to the right teams. The agent answers standard requests itself and prepares complex cases with a draft and context.
Frameworks & models
We build with n8n for orchestration and automation, LangChain for complex agent logic, and models such as Claude and GPT. The selection is driven by data protection, cost and task — not by hype.
Security & control
Least-privilege access, human-in-the-loop approvals, rate limits, prompt-injection protection and traceable logging of agent actions. Critical operations are designed to run only with a defined approval step.
Prototype & validation
We start with a tightly scoped use case, define success criteria and test on real data. Only after validated value do we gradually expand into production operation.
Operation & further development
Monitoring, analysis of error cases, ongoing prompt and tool optimization as well as model updates. Over time, your agent becomes more reliable, more cost-efficient and more capable.
Related Services
Matching services that complement your project
Frequently asked questions
The questions we are asked most often before a project – with answers on process, costs and data protection.
What is the difference between a rule-based bot, classic AI and an AI agent?
A rule-based bot follows hard-coded if-then paths and fails at anything that was not anticipated. Classic AI (e.g. an LLM) answers individual queries based on language understanding. An AI agent (agentic AI) goes further: it plans multiple steps, independently calls tools and systems, checks intermediate results and works through a task autonomously to the goal. We deliberately choose the appropriate level — not every process needs a full agent.
Can AI agents be used in a GDPR-compliant way?
Yes. We implement AI agents with clear data protection guardrails: EU hosting or data-protection-compliant model providers, least-privilege access to systems, approval steps (human-in-the-loop) for critical actions, logging, a deletion concept and a data processing agreement. Sensitive data remains under your control. Especially for SMEs, this controlled architecture is the prerequisite for productive use.
How quickly will I have a first working AI agent?
As a rule, we deliver a runnable prototype for a clearly defined use case within 2-4 weeks. This prototype works with real sample data, has defined success criteria and can be evaluated immediately. Only after validated benefit does the step-by-step rollout into production follow — this way you avoid large budgets without solid proof.
For which use cases are AI agents most worthwhile?
Processes with high volume and recurring structure are particularly economical: customer service (understanding inquiries, researching in CRM and knowledge base, creating tickets, escalating), sales (qualifying leads, researching, personalized outreach, CRM maintenance) and email triage (classifying the inbox, prioritizing, assigning and preparing drafts). We start with the use case that has the highest and most quickly measurable ROI.
Which technologies and frameworks do you use for development?
We work with n8n for orchestration and automation, LangChain for complex agent logic and tool use, as well as with powerful models such as Claude and GPT. We make the selection based on data protection requirements, cost and task — not by trend. The connection to your existing systems (CRM, ERP, ticketing system, calendar, email, databases) is made via defined, secured interfaces.
How is it prevented that an AI agent performs wrong or unwanted actions?
Through several control mechanisms: tightly limited permissions (least privilege), mandatory human approvals for critical operations, rate limits, protection against prompt injection and logging of agent actions. That keeps actions traceable and controllable. Critical steps are designed to run only with a defined approval step, and the agent becomes continuously more reliable through monitoring and the evaluation of error cases.
„Wir haben mit NovaCon AI zusammengearbeitet, die für uns eine E-Mail-Automatisierung und einen individuellen Chatbot erstellt haben, mit großem Erfolg. Die Automatisierung spart uns viel Zeit und macht unsere Prozesse deutlich effizienter. Der Chatbot entlastet unser Team spürbar und verbessert unseren Kundenservice durch schnelle, zuverlässige Antworten. Klare Weiterempfehlung!“Ready for the next step?
Let's discuss in a free initial consultation how we can move your business forward.