AI Architect
AI Architect
AI agents and AI in real business processes
I help companies move from AI experiments to production-ready systems: AI agents, customer service automation, reporting, sales operations and back-office workflows.
AI in practice
AI architecture that works in processes
I focus on use cases with measurable business value — not on technology for its own sake.
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AI agents
I design agents that support customer service, sales, research, lead qualification and day-to-day operational work.
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Data and security
I design the architecture so that data, roles, integrations and permissions are part of the project from day one — not an afterthought at the end.
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AI with n8n
I connect AI models with n8n workflows, APIs, CRM systems, forms, email, spreadsheets and the tools your company already uses.
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Phased implementation
I start with an MVP, measure results and expand the solution based on real process data — without burning budget on unproven assumptions.
AI implementation
From the AI idea to a working system
I help turn interest in artificial intelligence into safe, measurable and maintainable solutions that truly support business processes, not just look impressive in a presentation.
Discuss AI architecture-
Use case selection
Not every task should be automated with AI. I analyze processes, data, risk, cost of error and expected return — and select only the use cases that make operational sense. They can include AI agents for customer service, sales support, inquiry analysis, proposal generation, document extraction, research, reporting or automated summaries for teams.
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AI agent architecture
I design AI agents as part of a larger system — with a clear goal, scope of responsibility, access to the right data and safety rules. I define when an agent can act independently, when it should hand the decision to a human, and how its actions are logged. The result is a deployment that is controlled, auditable and ready for further growth.
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n8n, API and tool integrations
AI creates the most value when it is connected to the tools a company already uses. I connect language models with n8n, CRM, email, forms, spreadsheets, knowledge bases, ServiceNow and internal APIs. In practice, AI does not just answer questions — it launches workflows, classifies cases, passes data and updates systems.
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Security, quality and growth
In AI projects, the question is not just whether it works, but whether it works predictably. I design validation layers, logging, fallbacks, system instructions, prompt tests and simple effectiveness metrics. Implementation starts with an MVP and then grows based on data from real usage — which helps limit risk and show concrete business value faster.
AI outcomes
What does a well-designed AI architecture deliver?
The biggest value appears when AI supports a specific decision, process or role — and does not function as a separate technology island.
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Clear AI implementation roadmap
The company receives a list of priority use cases along with the required data, integrations and identified risks. AI stops being a broad idea and becomes a concrete action map with defined steps.
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Agents embedded in processes
An AI agent should know where data comes from, what it can do on its own and when it needs to hand the case to a human. That architecture not only reduces the risk of mistakes — it also helps the team actually trust and use the system.
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Knowledge and communication automation
AI can support customer replies, inquiry analysis, summaries, research, content generation and proposal preparation. The key is connecting the model with up-to-date company data — without that, even the best model answers out of sync with reality.
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Measurable business effects
Every implementation should have simple metrics: handling time, answer quality, number of cases solved automatically, process cost and team satisfaction. What gets measured can be improved.
Next step
Have a process worth automating?
Describe the challenge in a few sentences — I will reply with a concrete first step: an audit, an MVP or team training.