We build agentic AI solutions and train teams so they operate in enterprise environments: with access rights, quality, metrics, and operations.

You’ll talk to someone responsible for the outcome, not just someone giving a presentation.

On the first call, we define the scope: what to launch, which data sources can be connected, where the risks are, how to verify quality, and how to measure impact.

"from dialogue to action" in your systems

Integrations and orchestration

with quality control mechanisms

Employee assistants for IT, HR, finance, and operations

with access control and sources

Enterprise search and knowledge-based answers.

victory in the FinLanding category

2nd place

Winner

09.10.2019

GoTech 2019

03.12.2019

UrbanTech challenge 2019

21.12.2020

Archipelago 20.35, Superfinal

The project starts with KPIs and scope, not with development. That makes it easier to manage risks, quality, and timelines.

Engineering practice

Transparency

Measurable outcomes

We use modern approaches and tools where they truly accelerate implementation and support: n8n, MCP, tool calling, RAG, agentic AI, and our own frameworks for common use cases.

Timelines and budget are fixed before the start. We provide regular status updates on progress, risks, and next steps. No hidden add-ons or unexpected scope expansion.

Metrics and success criteria are documented in the work plan and deliverables for each stage, so it’s clear what counts as "done."

Choose an AI agent for your process

During a 20−30 minute, we’ll review your request, assess the implementation benefits, and propose a solution.

Need help?

We’ll answer your questions and help you choose the right solution. If there’s no ready-made agent for your use case, we’ll create one. Then we’ll configure it for your needs.