AI Solutions

What we build

AI solutions that earn their keep. Not demos, not buzzword-driven experiments — production systems with measurable business outcomes.

• Customer support automation — chatbots that handle tier-1 tickets with human handoff for complex cases. Trained on your knowledge base, your docs, your support history.
• Internal tools that replace repetitive manual work: document processing, data extraction, report generation, routine email handling.
• Data enrichment pipelines — taking raw business data and turning it into searchable, summarizable, queryable assets.
• Custom LLM applications — RAG systems, agents, semantic search, and structured output extraction.

How we think about AI

Most "AI projects" fail because they start with a technology and look for a problem. We start with the problem.

First question: is this actually an AI problem? Sometimes the right answer is a well-written regex, a state machine, or a better-designed form. If you're going to pay the cost of an LLM in latency, dollars, and unpredictability, it should be because no cheaper solution works.

When AI is the right fit, the second question is: which part of the problem is AI-shaped? Usually it's the natural-language bit — classification, extraction, summarization, generation. The rest should be deterministic code you can test and debug.

Our stack

LLM APIs (OpenAI, Anthropic) for most production workloads. We're heavy users of both and know their quirks.

RAG systems with pgvector, Pinecone, or Qdrant for knowledge-base and long-context applications. We design retrieval with the same rigor as any search problem — chunking strategy, embedding choice, reranking.

Tool use / function calling for agentic workflows. We know where agents shine (stateful, iterative tasks) and where they don't (deterministic pipelines).

Local models (Llama, Mistral) via Ollama or vLLM for on-prem or privacy-sensitive deployments.

Pricing

Proof-of-concepts start at €3,000 for a 2–4 week engagement. Production systems typically run €20,000–€80,000 depending on integration complexity. Ongoing retainers cover model maintenance, prompt tuning, and feature iteration.