LMXAI is an AI engineering & consultancy studio building LLM, agentic and RAG systems on sovereign, EU AI Act-compliant infrastructure — independent of hyperscalers, from prototype to production.
# EU-sovereign inference — no data leaves the cluster from lmxai import ServingStack, Agent stack = ServingStack( model = "mdAgent-Hermes-32B", # Qwen3-VL LoRA runtime = "vllm", quant = "awq-int4", gpus = 8, # A100 cluster region = "eu-sovereign", ) agent = Agent(stack, tools=["rag", "mcp"]) agent.serve(compliance="eu_ai_act") # illustrative API — not a published package
Most AI projects stall between a promising demo and a system that survives production. LMXAI closes that gap with end-to-end ownership.
Software engineering, AI architecture, data science, product strategy and commercialization — delivered by a single accountable partner acting as your fractional AI CTO.
Deep, hands-on engineering across the modern LLM lifecycle — model, system and strategy.
Custom models tuned for your domain and tool-use, then compressed to fit your hardware budget.
Reliable retrieval and multi-step agents with tool integration and rigorous evaluation.
High-throughput, cost-efficient serving on your own infrastructure — fully on-premise capable.
From POC to a commercialized product — architected to stay independent of hyperscalers and EU-compliant.
Read the AI Act guideA selection of shipped platforms across enterprise, allied health and education.
An enterprise AI workspace positioned against Copilot & ChatGPT Enterprise on EU data sovereignty — keeping company data inside the org's own boundary.
A multimodal tool-use fine-tune of Qwen3-VL-32B, optimized for reliable single-turn function calling and document understanding.
A FastAPI-based LLM gateway with token streaming, full observability and per-user namespace isolation for multi-tenant deployments.
How LMXAI builds reliable production agents — LangGraph orchestration, MCP tool integration, fine-tuned tool reliability (BFCL-v3), observability, and the real workspace tool ecosystem they operate.
A clinical AI platform for dietitians built on LangGraph, automating documentation and clinical workflows for regulated allied-health practice.
A personalized AI study companion for students — a reliable, fast-responding mentor that connects new topics to existing knowledge and supports teachers with content and planning.
Corpus statistics, LDA topic modeling and interactive topic-term visualization — the research layer underpinning Learning Matrix's semantic retrieval.
Where data sovereignty, compliance and reliability are non-negotiable.
SMB to large enterprise — internal copilots and AI workspaces.
Clinical AI for regulated healthcare and practitioner workflows.
Fintech, insurance, agriculture, customs & logistics.
EU AI Act-aligned, hyperscaler-independent deployments.
I'm an AI engineer and enterprise AI consultant based in Leiden, the Netherlands, working at the intersection of LLM research and production engineering. My approach is evidence-driven and skeptical but constructive — I care about systems that hold up under real load, not benchmarks that look good in a slide.
Tell me about your project — fine-tuning, agentic systems, sovereign deployment or AI strategy.
Leiden, Netherlands
info@lmxai.com
