AI Integration & AI Product Development in Berlin.
RAG pipelines and autonomous LLM workflows built into your product. Mojo Studio delivers ai products for founders and businesses in Berlin, senior engineers only, weekly demos, full IP ownership.
Built right,
shipped fast.
We integrate production-grade AI into real products — custom RAG pipelines over your own data, autonomous LLM agent workflows, and semantic search, built with the same engineering rigor as the rest of your stack. This isn't a chatbot bolted onto a landing page; it's AI wired into your actual product logic, with the guardrails to keep it from hallucinating in front of customers.
Built for Berlin's market.
Berlin's startup scene — one of Europe's largest by VC volume, home to companies like Zalando and Delivery Hero alongside a deep bench of earlier-stage climate-tech and deep-tech startups — runs leaner than London's on average funding size, making India-based development an attractive way to stretch a smaller seed or Series A round further. German engineering salaries, particularly in Berlin relative to Munich or Frankfurt, are more moderate than other major European tech hubs, but still command a substantial premium over India-based rates, and Berlin's startups are generally comfortable with distributed, English-speaking teams given the city's famously international workforce. Germany's strict data-protection culture, with GDPR enforcement taken unusually seriously here, means studios need to show real data-handling discipline, not just development speed, to win trust in this market.
Berlin's startup & vc-backed tech and climate tech businesses have specific needs when it comes to ai products — this is the local context that shapes how we scope every Berlin engagement, not a generic playbook applied everywhere.
From brief to launch.
Use-case scoping — where AI actually adds value vs. hype
Data pipeline & vector store architecture
Model integration (OpenAI, Gemini, Claude, or fine-tuned)
Evaluation, guardrails, and production monitoring
AI Products FAQ.
Grounded retrieval (RAG) over your actual data, tight prompt scoping, output validation layers, and explicit fallback behavior when the model isn't confident — hallucination is a design problem, not something you patch after launch.