Before we begin.

You’re working with an engineering partner who learns the process, connects your applications and builds the system around your team. We also plan how it will be tested, handed over and maintained. Explore what we build.

Something worth real time or money with a clear result: the replies your team writes every day, leads nobody has time to research, a report that takes a day to gather, or a process you’d like a system to run. One recent example is enough to start a useful conversation.

That depends on the process, data and integrations. After discovery, we propose a focused first phase with clear deliverables, a timeline and a price for you to review. We agree the scope before building.

Yes. We design and build custom AI agents for a specific job: answering customer questions, following up on leads, processing documents or keeping your CRM up to date. Each agent works from your own data, uses only the tools it is given, and hands anything outside its rules to a named person on your team. Explore what we build.

Yes. We build RAG (retrieval-augmented generation) systems that search your approved documents, price lists, policies and records, then prepare an answer that shows where it came from. The same knowledge base can power a customer support chatbot on your website or an internal AI assistant for your team. Explore what we build.

Yes. We build AI workflow automation in n8n when a visual, self-hostable workflow is the right fit, and in custom code when the process needs more control. Either way, you receive documented workflows, clear approval rules and a named owner for the exceptions.

Usually, yes. We connect AI and large language models to the tools you already use, such as Gmail or Microsoft 365, Slack, HubSpot and other CRMs, Google Sheets, databases and internal applications, through their APIs. We check what can connect during discovery, before proposing a system.

Systems for work that repeats (customer questions, email triage, document processing, CRM updates, reports and team handoffs), systems that find, research and follow up leads, AI agent teams with roles and budgets, research agents that cite their sources, and complete AI systems run end to end. We start with a scoped first phase and expand once it works. See six everyday examples.

Small and mid-sized companies that want practical AI automation rather than an AI strategy deck. We are based in Liepāja, Latvia, work in Latvian and English, and work with businesses across the Baltics and Europe, including Lithuania, Estonia, Finland, Germany, Norway, Poland and Sweden.

We start with the tools you already use. If an integration or access limit gets in the way, we explain the options before proposing a solution.

We test it against real scenarios, define what needs human review and make runs traceable. Missing information and unusual requests need a clear handoff to a person.

Yes. We can design the system to prepare drafts, updates or recommendations for review. Together, we define which actions need approval and which can happen automatically.

We map the sources and permissions needed for the agreed task. Access, data handling and any third-party services are discussed before implementation. Requirements vary by system, so these decisions are part of the scope.

We need input from people who understand the work, access to relevant examples and feedback during testing. We agree who is involved and what we need from them as part of the project plan.

The agreed working system, documentation and guidance for the people using it. We also agree ownership, access and an operating plan before launch. Read about the handover.

We agree on ownership, monitoring and maintenance before launch. Ongoing work can include investigating failures, improving quality and adapting the system as your business changes.