AI Systems · Architecture · Production
Lázaro
de Pina Ramos
Building AI that holds in production.
The distance between an AI demo and a system that runs reliably at 2am on a Tuesday with real users is where I spend most of my time. I design and build multi-agent orchestration systems, full-stack applications, and real-time ML pipelines. In production, not in notebooks.
I work in two modes that feed each other. As an external architect for companies that need a system built, reviewed, or rescued. And as a founder of ventures where I carry the outcome myself, so I live with my own architectural decisions long after a consultant’s report would have been filed.
01 — Why this combination
Depth in everything.
Superficiality in nothing.
Working across worlds that seldom touch is rare: artificial intelligence, blockchain, security, software engineering. Going genuinely deep in each one is rarer. Combining them produces a way of seeing that single-domain specialists never get.
A security background changes how you design an AI system, because you think about adversarial inputs and failure modes from the architecture phase instead of after the incident. A blockchain background changes how you think about state, consistency, and trust boundaries. Full-stack experience means the architecture I propose is one I can also build.
Depth
Never superficial. Every domain taken to production level, not to conversation level.
Clarity
Complexity resolved, not hidden. The simplicity that only comes from mastery.
Warmth
Technology with soul: honesty, keeping my word, and building with purpose.
02 — Capabilities
From architecture to production.
Multi-Agent AI Systems
Orchestration architectures where specialised agents run real workflows at production scale. Reliable coordination, not chained prompts.
LLM & RAG Integration
Retrieval-augmented generation pipelines, prompt engineering, and multi-provider LLM architectures built to survive real edge cases.
Full-Stack Engineering
React, Next.js, Node.js, Python, TypeScript. From interface to backend, owned end to end.
Real-Time Data Pipelines
Event-driven architectures, time-series processing, and low-latency inference systems.
Technical Architecture Review
Finding what breaks at scale before it breaks. System design, failure modes, and production hardening.
03 — Selected work
Built, not theorised.
Company names, product names, and complete technology stacks are not disclosed, some under NDA and the rest as standard practice. What is disclosed is what each system does, what problem it solved, and what was technically interesting about it. That is the part that matters.
04 — Engagement
Two ways in.
For companies
Your AI system needs to work in production, not just in the demo.
Three fixed-scope engagements: a four-week automation sprint that takes a manual process to a working AI system, a one-week architecture review that catches design problems before they reach production, and a monthly retainer for senior AI direction without a full-time hire.
See consulting engagementsFor investors
You back technical founders early.
I build ventures on the same foundations I build client systems on: deep tech with a defensible technical wedge, in markets where the hard part is engineering rather than distribution. Currently developing a multi-modal biomarker fusion engine for early disease detection.
See ventures05 — Current venture
Dei Corpus. Reading disease risk across domains.
The earliest warning signs of several major diseases emerge across physiological domains, not within one. Yet cardiovascular, metabolic, and dermatological signals are still captured and read in isolation, so those patterns go unseen until a condition is already symptomatic.
Dei Corpus is a multi-modal biomarker fusion engine: it correlates signals from already-certified sensors across domains to surface the risk patterns single-modality tools miss. Currently at TRL 4, with end-to-end feasibility demonstrated on synthetic datasets. The full thesis, status, and roadmap are on the Ventures page.
Let’s get your system to hold in production.
Whether you need a system built, an architecture reviewed, ongoing technical direction, or a conversation about a venture, the starting point is the same: tell me what you are trying to solve.