What We Build

We build production-grade AI features for startups and small-to-mid companies. The brief is usually the same: "we want AI in the product, but the agencies we've talked to all want to sell us the same chatbot demo." We don't do that.

• Custom AI agents that act inside your product — not generic copilots bolted onto a sidebar
• RAG systems trained on your data: docs, tickets, codebases, knowledge bases, internal wikis
• LLM features integrated into existing apps: drafting, summarization, classification, extraction
• Workflow automation that uses LLMs where they actually add value, not as window dressing
• Private LLM deployments on your own infrastructure when data can't leave your perimeter

Built by Engineers, Not Hype

AI features are easy to demo and hard to ship. The hard parts — evaluation harnesses, retrieval quality, token budgets, fallback paths, observability, cost control — are what make the difference between a slick demo and something you'd put in front of paying customers.

We focus on those parts. The result is AI integrations that hold up in production, not pilots that quietly get shelved after the launch announcement.

• No off-the-shelf wrappers — every agent and pipeline is built for your data and your product
• Real engineering rigor: tests, evals, monitoring, retries, graceful degradation
• Honest about what LLMs can't do — we'll tell you when a problem is better solved without AI
• From PoC to production: most AI projects die at the demo stage, ours don't

Stack & Tooling

We're model-agnostic and pick the right tool for the job. For most projects that means a frontier model (OpenAI GPT, Anthropic Claude, Google Gemini) with a clear migration path; for sensitive data it often means open-source models (Llama, Mistral, Qwen) running in your VPC.

Models: OpenAI, Anthropic Claude, Google Gemini, plus open-source via Ollama, vLLM, or Hugging Face. Frameworks: LangChain, LlamaIndex, plus plenty of plain Python when frameworks get in the way. Vector & retrieval: Pinecone, Weaviate, Qdrant, pgvector, hybrid search with reranking. Eval & observability: LangSmith, Langfuse, custom eval harnesses. Infrastructure: AWS, Hetzner, Docker — wherever your data lives.

Your Data Stays Yours

GDPR-compliant by default — we're an EU company with EU data residency, and we don't ship your data to third parties without an explicit, contracted reason. For sensitive workloads we deploy private LLMs on your infrastructure, so prompts and embeddings never leave your network.

Where you sit on the spectrum is your call: managed API providers when speed matters, private deployment when sovereignty matters. We'll help you make that call based on your actual data, your actual users, and your actual regulatory exposure — not on FUD.

• Private LLM deployment on your AWS, Hetzner, or on-prem infrastructure
• Zero-retention configurations with API providers when private deployment is overkill
• DPAs in place, EU data residency, no training on your data
• Clear data flow diagrams so your DPO and your customers know exactly what goes where

How We Work

Same model as the rest of our work: small senior team, no juniors, direct access to the engineers writing the code. For AI projects we usually start with a tightly-scoped PoC — two to four weeks to prove the approach works on your real data — then scope the production build from there.

Start with a free technical assessment: tell us what you're trying to build, share what you can about your data, and we'll send back a written response within 24 hours covering whether AI is the right fit, which approach we'd take, rough timeline, and where the risks are. No sales call required.