AI applications that
run in production

The problem

Generic chatbots don't know your documents and fill in the gaps with whatever they don't know. For legal, medical, technical, or policy work that's unusable: one fabricated source and trust is gone.

Our approach

• Hybrid retrieval: semantic search (vectors) combined with keyword search (FTS) for maximum coverage
• Reranking that puts the truly relevant passages at the top
• Citation verification that checks whether the answer actually appears in the source
• Source cards: every answer with a clickable link to the original
• Evaluation harness with a golden set — hallucination rate is measured, not hoped for

Result. Answers with a clickable source in milliseconds, and a measurably low hallucination rate.

The problem

Repetitive work eats hours: retyping data between systems, sorting mail, follow-ups that stall. People are too expensive and too valuable for work a machine does better.

Our approach

• n8n workflows that connect your systems (mail, CRM, database, API)
• AI agents that execute tasks autonomously and escalate when needed
• Human-in-the-loop at the points that matter — control stays with you
• Monitoring and logging so you always see what is happening

Result. Hours back every week, fewer errors, and processes that keep running at 3 a.m.

The problem

Classic SEO is no longer enough. More and more, people ask ChatGPT or Gemini their question. If you are not mentioned there, you don't exist for that user.

Our approach

• Technical SEO: speed, structure, schema.org and a clean information architecture
• GEO (Generative Engine Optimization): content AI models understand and cite
• llms.txt and markdown variants so AI agents can read your site reliably
• Measuring your visibility in generative answers, not only in the SERP

Result. Visibility in search engines and in generative AI — the two places where tomorrow is searching.

Where your data cannot leave the building, the entire chain runs locally: embedding, retrieval and the model itself on hardware you control, in the Netherlands or the EU. Per component you get an overview of what runs where — not a promise, but a floor plan.

This is not theory: our own lab runs partly fully on-prem, from the continuous agent memory to the case-law pipeline with sensitive sources.