I build AI systems that don't fall apart in production.
Most AI projects look great in demos. They collapse when real users arrive. I help CTOs, founders, and engineering teams design and ship AI systems that actually hold up — with the architecture, reliability, and documentation to prove it.
Does any of this sound familiar?
These are not edge cases. They're the default outcome when AI projects skip architecture and go straight to implementation.
"It worked perfectly in the demo."
Your LLM system performed beautifully in controlled conditions. Production traffic, messy real user inputs, and unexpected edge cases exposed the gap between prototype and product.
"Our RAG keeps returning wrong answers."
Retrieval-Augmented Generation is not plug-and-play. Chunking strategy, embedding model choice, re-ranking, and context window management all require deliberate architecture decisions.
"We have AI in the roadmap but no one who can own it."
Your engineering team is strong, but nobody has shipped a production LLM system before. You need senior guidance without the cost and delay of a full-time hire.
"Our automation breaks every time something changes."
Agent workflows and automation pipelines built without proper error handling, observability, and fallback logic fail silently — and team confidence erodes every time.
"Hallucinations are killing our credibility."
Unconstrained LLM outputs in customer-facing flows are a trust problem. Grounding, output validation, and feedback loops are not optional — they're architectural requirements.
"We're scaling and the system is buckling."
What worked at 100 requests per day breaks at 10,000. Token limits, latency, costs, and rate limits need to be designed for — not discovered in production.
Strategic AI architecture for systems that have to work.
RAG Architecture & Design
End-to-end retrieval pipeline design: data ingestion, chunking strategy, vector store selection, embedding optimization, re-ranking, and hallucination containment.
LLM System Design
Prompt engineering at the architecture level, model selection, context management, output validation, and reliability patterns for production LLM deployments.
Agent Workflow Architecture
Multi-agent orchestration, tool use patterns, human-in-the-loop design, and error recovery systems that don't require constant babysitting.
AI Infrastructure Decisions
Infrastructure trade-offs: self-hosted vs. API-based models, caching strategies, cost optimization, latency budgets, and monitoring setup.
Architecture Reviews
Structured review of your existing AI implementation — finding failure modes, architectural debt, and scalability blockers before they hit production.
Technical Advisory & Fractional Leadership
Ongoing strategic guidance for engineering teams and leadership: architecture decisions, vendor evaluations, team upskilling, and AI roadmap ownership.
How we can work together.
Every engagement starts with understanding the actual problem — not the reported symptom. Scope, timeline, and deliverables are defined before any work begins.
AI Architecture Assessment
A structured deep-dive into your current or planned AI system. I identify architectural gaps, reliability risks, and scalability blockers — and deliver a written report with prioritized recommendations you can act on immediately.
- →Codebase and architecture review
- →Written assessment report with prioritized findings
- →Architecture decision recommendations
- →One follow-up session to walk through findings
Production AI System Build
Hands-on architecture and implementation of a production AI system: RAG pipeline, LLM integration, agent workflow, or automation infrastructure. I build it, document it, and hand it off working.
- →Architecture design and implementation
- →Integration with your existing stack
- →Error handling and observability setup
- →Full documentation and handover
Fractional AI Architect
Ongoing strategic and technical guidance embedded in your team. I own AI architecture decisions, review implementations, advise on vendor and model choices, and serve as the senior technical voice your team needs to ship confidently.
- →Weekly architecture reviews and guidance
- →Async availability for technical decisions
- →Quarterly architecture roadmap sessions
- →Team office hours and design reviews
AI Project Rescue
Your AI project is stuck, broken, or about to go live with known problems. I come in, diagnose what went wrong, and build the path forward — whether that means fixing what's there or re-architecting the critical parts.
- →Root cause analysis and written diagnosis
- →Immediate stabilization recommendations
- →Prioritized remediation roadmap
- →Optional hands-on remediation
Why most AI projects fail in production.
After two years of building and reviewing AI implementations across dozens of companies, the failure patterns are consistent. None of them are mysterious.
Architecture decisions made by the wrong people at the wrong time.
LLM system architecture gets decided by developers under sprint pressure instead of by someone with production experience. By the time the problems surface, the architecture is already baked in.
Retrieval isn't treated as an engineering problem.
Most RAG implementations use default chunking, default embeddings, and no re-ranking. The result is a system that returns plausible-sounding wrong answers with confidence. Fixing this after the fact is expensive.
No observability, no feedback loops.
Teams deploy LLM features with no way to measure whether they're working. Without logging, evaluation pipelines, and user feedback mechanisms, you can't improve what you can't see.
Prompt engineering treated as a magic input, not a design surface.
A good prompt is a system specification. When prompts are written ad-hoc and not maintained like code, they drift, break with model updates, and become impossible to debug systematically.
No plan for what happens when the LLM is wrong.
Production AI systems need graceful degradation, output validation, and fallback logic. Systems built without these patterns fail loudly or — worse — silently, in ways users notice before you do.
Related reading: I wrote a detailed breakdown of this pattern: Why AI Projects Fail After the Demo.
Structured. Documented. Direct.
I've built AI systems for production environments for long enough to know where most engagements break down — and it's rarely the technology. It's unclear scope, missing documentation, and no definition of done.
Diagnosis
Before I write a line of code or a single recommendation, I need to understand what's actually broken. This means access to the codebase, system logs, and an honest conversation about what's been tried and what failed.
Architecture
I define what we're building, what's explicitly out of scope, and what "done" looks like. Architecture decisions are written down before implementation starts. No sliding scope.
Build
Implementation with the error states, edge cases, and observability that production environments demand. I work in focused blocks — not in continuous Slack threads.
Handover
Everything I build gets documented as part of the deliverable: architecture decisions, integration guides, runbooks. The goal is a system that works without me. If you need me again, it's for the next problem.
I'm remote-first, based in São Paulo (UTC-3). I communicate through structured written updates, not constant availability. You'll always know what I'm working on and when it'll be ready.
Read: How I Work →Production AI systems, not presentation slides.
I don't have case studies with projected outcomes. These are things that shipped, that ran on real traffic, and that are still running.
Selected work
1st Place — ByteDance Global Coze AI Challenge
Built "Auty", an AI agent for autism support, winning a global competition against hundreds of submissions. The system still runs six months later.
Read case study →RAG Chatbot with 40% Ticket Reduction
Designed and built a production RAG chatbot for a WooCommerce store that cut support ticket volume by 40% while handling real customer queries at scale.
Read case study →Autonomous SEO Intelligence Pipeline
Built an end-to-end AI pipeline connecting Google Search Console, an LLM analysis layer, and automated WordPress publishing — running without manual intervention.
Read case study →“Paulo has outstanding skills to organize and communicate demands in situations that seem absolutely chaotic. He has a lot of technical knowledge and is able to communicate efficiently with both technical and lay people.”
“Paulo is a great sysadmin. Every website and blog that Paulo has taken care of never crashed, even during traffic spikes with thousands of visits. The main reason for keeping him was not just his technical competence, but the fact that he is one of the most reliable people I've met in my entire life.”
Companies I've worked with
Recognition
- ▸1st Place — ByteDance Global Coze AI Challenge (2024)
- ▸Technical book published — 4.6★ on Amazon (still in print)
- ▸17,000+ developer followers on Dev.to
Speaking & Teaching
- ▸Campus Party Brasil — Speaker (2009, 2010, 2011, 2012)
- ▸Sebrae Empreendedor — Speaker, Belém (2010)
- ▸Senac Franca — Instructor (2009)
- ▸Apadi — WordPress instruction (2010–2013)
- ▸ComSchool — WordPress instruction (2014–2016)
Common questions.
What is an AI architecture consultant?
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How is this different from hiring an AI development agency?
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What does a fractional AI architect actually do?
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My team already has LLM experience. Why would I need this?
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What does a RAG architecture review cover?
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Do you work with teams outside Brazil?
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How long does an AI architecture assessment take?
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What if our AI project is already broken?
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Your AI project deserves production-grade architecture.
If you're building AI systems that need to work reliably — under real traffic, with real users, with real consequences for failure — I can help you get there. Start with a short conversation about where you are and what you're trying to solve.
No pitch decks. No discovery call with a sales team. You talk to me.