Berlin teams come to us when an AI prototype meets real operations: customer data, GDPR, an internal team that needs to trust the output. We add AI features inside SaaS products, custom platforms and internal tools — assistants, document extraction, smart search, workflow automation — engineered to live alongside your existing systems, not to replace them on a slide. When AI is the wrong tool, we build the platform without it.
Berlin is Europe's applied-AI hub: the largest concentration of LLM, agent and product-engineering teams between London and Paris, with founders shipping out of Mitte, Kreuzberg, Charlottenburg and the corridors around Factory Berlin and Mindspace. Add the regulators (BfDI, BMI), the customers (Bundesdruckerei, ImmoScout24, N26, Trade Republic, Zalando) and the EU-hosting infrastructure those teams have to ship through, and you get a market where 'AI in production' has a real meaning. Architecture-first, EU-hosted by default, designed to survive the first audit — whether your product needs an AI feature or just a solid custom platform.
Four engagement shapes we deliver in Berlin. Each one starts from a real workflow your team runs today — not from a model demo. AI is the right tool for some of them and the wrong tool for others; we say so up front.
AI agents and copilots that sit inside your operations — sales, support, ops, research. Connected to your real data sources (CRM, internal docs, ticketing), with role-based access, audit logs and EU-only inference. Designed to be useful from week three, not impressive in a demo.
End-to-end automation of recurring workflows: lead routing, contract drafting, invoice processing, customer onboarding. We model the actual workflow first, then add LLM steps where they remove human work — not where they add a chatbot for show.
Extracting structured data from PDFs, contracts, invoices, regulatory filings and internal docs. OCR plus LLM verification, with deterministic fallback paths. Built so that a wrong extraction is logged and reviewable, not silently passed downstream.
EU-hosted model serving, prompt and PII redaction, retention controls, sub-processor disclosure, DPA-ready architecture. Whether you use OpenAI, Anthropic, Mistral, or a self-hosted model, we design the data path so your customers' procurement team can sign it.
Three engagements where AI moved from prototype to production — each one with a real ops or compliance constraint.
Startup EngineeringLead Lab - B2B Revenue Operations Platform with Automation & Intelligence FeaturesCustom B2B revenue operations platform for structured growth, experimentation and CRM-centric workflows — with optional automation and AI-assisted intelligence layered on top, under human oversight.Read plate →
Digital Experience & Brand SystemsForschungsmittel.comB2B funding website and connected product platform with client dashboard, team workspace, document workflow, and operational command center.Read plate →
Enterprise-Grade FoundationsVulken FMInspection & Asset Management Platform - Internal survey and compliance system for facilities management with mobile inspection app and web-based admin platform.Read plate →Yes — that is the default. We design every Berlin engagement around EU-hosted inference: AWS eu-central-1, Azure West Europe, Mistral, or self-hosted models on EU-resident GPUs. OpenAI and Anthropic are used through their EU data-residency tiers when appropriate, with documented sub-processor disclosure ready for your customer's procurement team.
That is the most common shape of work. We integrate with the systems already in place (HubSpot, Pipedrive, Salesforce, internal CRMs, custom ops dashboards) rather than replacing them. The AI sits alongside as a new capability — assistants, automation, extraction — not as a parallel system that fights yours for state.
We default to frontier models (GPT-5, Claude, Mistral Large) for general reasoning and use cases where pre-training matters. For domain-specific extraction or classification, we fine-tune smaller open models when the data and economics justify it. We do not build a model from scratch unless there is a regulatory or moat reason — most teams need integration, not a foundation model.
Every production path has a verification step: structured output schemas, deterministic validation, source citations, and a human-review queue for low-confidence answers. The architecture treats the LLM as a probabilistic component, not as the source of truth. Wrong outputs are logged, reviewable, and traceable to the prompt and source data that produced them.
After the 5-day Architecture Sprint, a first production-ready release is typically 6–10 weeks for a focused workflow (one assistant, one extraction pipeline, one automation), 3–5 months for a multi-workflow operations layer. We deliberately avoid 'AI everything everywhere' scope creep — the first release ships one workflow end-to-end, then we expand.
We ship out of Berlin into the other markets with on-site kick-off, the Architecture Sprint on the ground, and live pair-time through implementation.
Five days. €3,500. We map your existing systems, name the data and compliance risks, and hand you an AI roadmap your team — or ours — can execute.
