01 / Agents
Agents & MCP servers
Multi-step agents with tool calling, structured outputs and human-in-the-loop approvals. MCP servers that plug them into real business tools and data.
LangChainLangGraphMCPTool calling
I BUILDAGENTSTHAT SHIP
Previously at·Rolex·Helvetic Payroll·Grow Surely·42 Lausanne·Business School Lausanne·AI builder·Switzerland·Previously at·Rolex·Helvetic Payroll·Grow Surely·42 Lausanne·Business School Lausanne·AI builder·Switzerland·
Live · talk to my agent
Meet rex.ai
Talk to my agent, built on my own agent harness. Ask about my work, or have it message me or book a call.
rex.ai
Lucca's agent · online
Hi, I'm rex.ai
Lucca's agent. Ask me about his work, projects and stack, or I can find a slot on his calendar.
tools: check_availability() · book_meeting() · send_lucca_a_message()
AI agent · can make mistakes
Prefer a human? Book a call ↗
// "just make it work in prod." \\
What I build
for teams like yours.
02 / Retrieval
RAG & document pipelines
Retrieval systems and vision-language pipelines that turn thousands of messy enterprise files into validated, structured data.
RAGpgvectorAzure AI SearchVLMs
03 / Automation
Workflows that run themselves
Automations that connect research, enrichment, CRM and outreach, mixing LLM output with plain business rules where each one fits.
n8nWebhooksHubSpotAPIs
Built in public · my own agent harness runs this site's agent
Stack
- Python
- TypeScript
- LangChain
- LangGraph
- MCP
- FastAPI
- Next.js
- Postgres + pgvector
- Docker
- GCP / Vertex AI
- Azure AI Search
- Gemini
- n8n
- Vercel AI SDK
- GitLab CI/CD
The record
Three roles.
One habit: shipping.
AI Engineer ConsultantHelvetic Payroll (for a luxury watch manufacturer) · Switzerland · Sep 2025 – Jun 2026+
- Built production GenAI assistants and multi-step agent workflows in Python and JavaScript, owning implementation, debugging and iteration end to end.
- Built MCP servers and REST API integrations connecting AI agents to business tools and data, using tool calling, structured outputs and reusable components.
- Built RAG systems and AI-powered internal tools, turning ambiguous business needs into working automations with stakeholders.
Digital Innovation InternRolex · Geneva · Mar 2025 – Aug 2025+
- Built a Python document-processing pipeline handling ~6,000 enterprise files, automating extraction, transformation and validation with vision-language models and structured outputs.
- Benchmarked AI models and providers on quality, reliability, trade-offs and business fit to guide tooling decisions.
- Evaluated emerging AI tools and turned findings into implementation recommendations for business teams.
Sales EngineerGrow Surely · Remote · Feb 2024 – Mar 2025+
- Built and launched 8+ automation workflows with n8n, HubSpot, APIs and enrichment tools, covering research, enrichment, outreach and follow-up.
- Built AI-assisted workflows combining LLM outputs, structured rules and business logic to automate repetitive operations and personalization.
- Designed scoring and prioritization logic and iterated on it directly with clients.
42 LausanneComputer Science & Software Development2024 – 2026
Business School LausanneBachelor, Business Administration2021 – 2023
By the numbers
Small numbers, real systems. Everything here ran on actual company data.
~6,000
Files through one pipeline
Enterprise documents extracted, transformed and validated with vision-language models at Rolex.
8+
Workflows launched
Production automations across research, enrichment, CRM and outreach at Grow Surely.
MCP
Servers in production
Agents connected to real business tools through MCP servers and REST integrations.
4
Languages
English, French, Portuguese and Spanish, so the stakeholder call doesn't need a translator.
Biz + Eng
Two backgrounds
A business degree and 42 Lausanne. I can read the P&L and the stack trace.
0 → prod
End-to-end ownership
Scoping, building, debugging and iterating, not just the demo.
General
What do you actually build?+
Agents that do real work: assistants with tools and approvals, MCP servers that connect them to business systems, RAG over company data, and the automations around all of it. Most recently for a luxury watch manufacturer, as a consultant with Helvetic Payroll.
What's your stack?+
Python and TypeScript. LangChain/LangGraph for agents, FastAPI and Next.js around them, Postgres + pgvector for memory and retrieval, Docker, and GCP or Azure depending on the client. n8n when a workflow tool is the right answer.
Where are you based?+
Switzerland. I work in English, French, Portuguese and Spanish.
Can I just ask your agent?+
Yes, that's the point. rex.ai, just below the intro, knows my experience and projects, can pass me a message, and can book a call on my calendar.
How do I reach a human?+
Book a slot with the button on this page, or email luccafmourao@gmail.com. I answer quickly.