AI engineering for Amsterdam, from Science Park to Zuidas.
RAG, agents, evals, observability, streaming UIs. We ship AI products from a senior team in Groningen, on the same business day as the Amsterdam AI cluster. Spin-outs from UvA Science Park, post-Series-A scale-ups bolting on AI features, internal-tools teams replacing back-office work: we've shipped the shapes.
What we build
RAG over your own data
Document ingestion, chunking, hybrid retrieval, reranking, citation traces. pgvector first, vector DBs second, evals from day one.
Production-grade agents
Tool calls, structured outputs, retries with budgets, deterministic test paths. Agents that run unattended with audit trails.
Streaming + cancellable UIs
Vercel AI SDK patterns, token-by-token rendering, cancellable streams, optimistic state. The UX visitors now expect.
Costs + latency observability
Token tracking, model A/B, latency dashboards. AI is a production system; we instrument it that way.
Where this fits
You're an Amsterdam scale-up bolting an AI feature onto a React + Node app and the team has shipped CRUD, not LLMs.
You're a Science Park spin-out with a strong model and a weak product; we close the gap.
You're a B2B SaaS adding an AI assistant on internal data and you want it observable before it ships.
Tech stack
- TypeScript
- OpenAI / Anthropic
- RAG pipelines
- pgvector
- Vercel AI SDK
- BullMQ
Want this for your team?
30 minutes with a founder or senior engineer. We'll scope what you need and tell you straight whether Stacklane fits.
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