AI integration that ships, not AI theater.
We integrate AI into real products — LLM features, document intelligence, retrieval, agents, and on-device models — with evaluation suites and cost controls, not demos that die in a slide deck. Senior European engineers, full-time overlap with your US or UK workday.
We ship AI in our own products.
Before we integrate AI into your product, look at how we use it in ours — shipped software, not concept videos.
libro — local document AI
Personal accounting that AI-parses financial PDFs and matches payments to invoices — entirely on-device. No SaaS, no data leaving the machine.
Bridge — agent-driven ops
The console we run our own studio on: AI agents handle research, drafting, and busywork under human review.
An AI-native workflow
AI is wired into every layer of how we build — design, code generation, testing, review — which is why our quotes come in lower.
We build native Mac apps too — several are free to download, so you can judge our engineering before you hire us.
From one LLM feature to a full AI workflow.
LLM features in your product
Summarization, extraction, classification, drafting, translation — the focused features your users actually use, built into your existing web, mobile, or desktop app.
Document intelligence
PDFs, invoices, contracts, and scans turned into structured data — parsing pipelines with human-review fallbacks where accuracy is critical.
RAG & knowledge search
Ask-your-data search over your docs, tickets, and wikis — retrieval pipelines with citations, permissions, and freshness handled properly.
AI agents & automation
Multi-step workflows that research, draft, reconcile, and file — with guardrails, audit logs, and a human in the loop where it matters.
Local & on-device AI
When data can't leave the building: open-weight models on your infrastructure, or on-device AI on Apple Silicon — like our own libro.
Model strategy, evals & cost
Provider selection, evaluation suites, model routing, caching, and token budgets — so quality is measured and the invoice is predictable.
Production AI is an engineering problem.
- Evals before launch. Every AI feature ships with an evaluation suite on your real data — so "it seems good" becomes a number you can track.
- Costs designed, not discovered. Model routing, caching, and token budgets from day one. You'll know each feature's unit cost before it ships.
- Privacy by default. EU-based team, GDPR-grade handling, zero training on your data, and local-model options when data can't leave your infrastructure.
- No provider lock-in. Claude, OpenAI, Gemini, or open-weight — behind an abstraction that lets you switch as the market moves.
- Honesty about fit. If a regex, a cron job, or a plain database query beats an LLM for your problem, we'll tell you on the first call.
Need the app around the AI as well? We build native macOS apps and React Native apps for iOS & Android — AI included where it earns its keep.
Prototype in weeks, hardened for production.
Use-case scoping
30 minutes. We find where AI actually pays off in your product — and tell you where it doesn't.
Prototype on your data
A working feature on your real data in 1–2 weeks, with a fixed USD quote up front.
Evals & hardening
Evaluation suite, guardrails, fallbacks, cost controls — the unglamorous work that makes AI reliable.
Ship & monitor
Production rollout with quality and cost dashboards, plus iteration as models improve.
Senior EU engineering, on your clock.
Questions founders ask us about AI.
Which AI models and providers do you work with?
Anthropic Claude, OpenAI, and Google Gemini for hosted models, plus local open-weight models when data can't leave your infrastructure. We pick the model per use case based on quality, latency, and cost — and design the integration so you can switch providers without a rewrite.
Can AI features run without sending data to the cloud?
Yes. We build local-first AI where it fits: on-device models on Apple Silicon and local pipelines for sensitive documents. Our own accounting app, libro, parses financial PDFs with AI entirely on the user's machine — no SaaS, no data leaving the laptop.
How do you keep AI API costs under control?
Cost is an engineering constraint from day one: model routing (small models for simple calls, frontier models where quality matters), caching, batching, token budgets with alerts, and per-feature cost dashboards. You'll know the unit cost of every AI feature before it ships.
Do we actually need a chatbot?
Usually not. Most products get more value from targeted AI features — summarization, extraction, search, classification, automation — than from an open-ended chat window. If AI isn't the right answer for your problem, we'll say so on the first call.
How fast can we have a working prototype?
A working prototype on your real data typically takes one to two weeks. We then harden it with an evaluation suite, guardrails, and cost controls before production. Scoping call first, fixed quote in USD within 48 hours.
Who owns the code, prompts, and evaluation data?
You do — code, prompts, evals, and fine-tuned artifacts, from the first commit. We sign your NDA before the first call, and your data is never used to train anyone's models.
Have an AI feature in mind?
Tell us the problem — not the model. You'll get a reply from the engineer who'd build it, within one business day, in your time zone.
Scope your AI feature →