AI-nativeproducts,in production.

An AI agent doesn't just generate text: it calls tools, makes decisions, and completes a task across multiple steps. With MCP servers I wire it to your own systems (invoicing, banking, calendar, CRM), so the agent does real work, not just gives advice. My first such product is titkar.ai.

An AI integration is a feature: a chatbot in an existing e-commerce store, for example. In an AI product, the AI is the foundation: Phora AI generates lifestyle product photos without the user ever seeing a prompt. There the AI isn't a feature; it's the value proposition.

A simple chatbot or content generator is roughly €1–2k. A multi-step agent workflow €5–10k. A production-grade AI product €12k and up. The cost is mostly not development; it's context engineering and the cost/rate-limit layer underneath.

Yes, through fine-tuning, or through retrieval-augmented generation (RAG). In practice RAG is usually enough: a vector database (Pinecone, pgvector) holds the knowledge base, and the LLM only sees relevant context per query. Anthropic and OpenAI both support zero-retention mode for sensitive data.

When deterministic output is required and a simple rule system already solves it. When errors aren't acceptable (financial transactions, legal decisions). When the input is so structured that the LLM is pure overhead. AI isn't the default; it's a tool for specific problems.