01
Agents for internal processes
An agent can search information, prepare data, summarize tickets, analyze documents or execute repeatable steps across your tools.
BackofficeResearchOperations
Agents, automations and AI features that run inside real products.
AI Engineering Your company does not need AI hype. It needs a working system.
I build AI agents, chatbots, internal automations and AI integrations that work with your data, tools and existing applications — cleanly engineered, controllable and ready for daily use.
Focus Agentic systems & automations
Use Chatbots, workflows, internal tools
Base LLMs, RAG, APIs, product integration
What AI Engineering means
Not just a prompt. A system that gets work done.
A useful AI agent is not a single chat box. It needs context, boundaries, tool access, logging, approvals and proper integration into existing software. That is where I work: from the first automation idea to a production-ready AI feature.
Use cases
Where AI can become practical in your company quickly.
02
Chatbot inside your existing app
An AI assistant directly in your product: answers questions, explains data, supports users and works with existing APIs instead of only chatting generically.
In-App AssistantSupportSelf-Service
03
Automations between tools
Email, CRM, databases, calendars, tickets or documents can be connected so repetitive work no longer has to be handled manually.
APIsWorkflowsTool Calling
04
RAG & knowledge search
Your documents, data and internal knowledge become searchable and usable: with sources, permissions and answers that stay traceable.
RAGVector SearchKnowledge Base
What I build
AI features that do not feel isolated, but fit into your product.
Agent Layer
Planning, tool selection, safety boundaries and controlled execution for concrete tasks.
Chat Interface
A clear UI for users: ask questions, inspect results and approve actions.
Context & Data
Connect documents, databases, APIs and product state so the AI has relevant context.
Integration
Embed into your existing web app, admin area, API or internal software.
Observability
Logging, cost control, debugging and feedback loops so operations stay measurable.
Process
From automation idea to controlled AI system.
Sharpen the use case
We identify a concrete task where AI creates real value — not just a demo.
Connect data & tools
Which systems can the AI read, which actions can it execute, and where is human approval required?
Build a prototype
A small usable agent or chatbot quickly shows whether the workflow works.
Make it production-safe
Guardrails, logging, roles, fallbacks and deployment turn the prototype into a reliable feature.
Common requests
Does one of these sound like you?
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We want an agent that understands internal documents and answers with sources.
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Our customers should ask questions inside the app and get direct help.
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We repeat the same manual process every week and want to automate it.
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We have data and APIs, but no useful AI layer on top.
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We want to use AI safely, traceably and without a black-box feeling.
Stack & building blocks
Technical enough for production. Flexible enough for new ideas.
LLM Apps
Chat Interfaces · Streaming · Function Calling · Structured Outputs
Agents
Tool Use · Planning · Approvals · Memory · Workflow Orchestration
Data
RAG · Embeddings · Vector Search · SQL · Documents · Permissions
Engineering
TypeScript · .NET · APIs · Docker · Observability · Deployment
AI / NEXT STEP
Do you want to build an agent, chatbot or automation?
Send me the process, tool or app where AI should help. I will reply with a concrete view of what is worth automating and what a first prototype could look like.
Send AI idea
Directly to [email protected]