AI Agent Development that works for you

A chatbot answers questions within a predefined conversational flow. An AI agent goes much further: it reasons about complex problems, breaks tasks into steps, uses external tools (APIs, databases, browsers) and executes actions autonomously. For example, a chatbot tells you the status of an order; an agent detects that the order is delayed, contacts the logistics provider, offers the customer compensation and updates the CRM, all without human intervention.

Virtually any task that follows a process with rules, even if it requires judgement and adaptation. The most common cases include: customer support with full resolution (not just answers), document and invoice processing, lead qualification and follow-up, data analysis and report generation, email triage, code pipeline automation, data reconciliation across systems and orchestration of complex workflows combining multiple tools.

Security is the central pillar of our agent architecture. We implement strict guardrails: action limits (what the agent can and cannot do), human approval for critical actions (human-in-the-loop), complete logging of all decisions and actions, and automatic rollback in case of error. Additionally, we deploy on your cloud infrastructure with end-to-end encryption and GDPR compliance. The agent always operates within the boundaries you define.

MCP (Model Context Protocol) is an open standard that allows AI agents to connect securely and in a standardised way with external tools: databases, APIs, file systems, browsers and more. Instead of building ad-hoc integrations for each tool, MCP provides a universal interface. This means your agent can connect to new tools without rewriting code, reducing maintenance costs and accelerating integration with your existing systems.

It depends on the agent complexity and required integrations. A basic agent with 2-3 tools can be in production in 4-6 weeks. A multi-agent system with multiple integrations and complex flows typically requires 8-12 weeks. Our methodology prioritises a functional prototype in the first 2-3 weeks to validate value before investing in the full solution. Typical ROI is achieved in the first 2-3 months thanks to the reduction of manual tasks.