Álvaro Bastos

I started in aviation: I hold a degree in Aeronautical Sciences and I am an aircraft pilot. That is where I learned that critical systems run on checklists, redundancy and rule-based decisions, not improvisation. Then came executive leadership in financial operations and transformation and, with it, hands-on software engineering and applied AI. So I work on two levels at once: I understand the real process, with its risks and constraints (compliance, audit, segregation of duties, data privacy), and I turn it into a production system with sustainable architecture, integrations and operations.

At ALIS LABS, I build products on a multi-tenant SaaS architecture designed to scale with control: organization isolation, data governance and permissions, and audit trails from day one. That includes authentication and organizations, billing and plan controls (who can use what, with which limits) and data pipelines for end-to-end automation: ingestion, normalization, reconciliation, enrichment and APIs.

I apply Agentic AI and LLMOps patterns to run AI predictably: agent and tool orchestration (planner, executor, verifier, router), tool use to connect APIs and systems, and goal-oriented execution with verification. For knowledge, I use production RAG (vector search, keyword and rerank), with a versioned corpus and explicit criteria for citing sources. For decisions and security, I route by intent and risk, with clear guardrails: scope, validation, per-tenant limits, leakage protection and privacy by design (minimization, masking of personal data, retention and rationale).

The foundation is production engineering: event-driven architecture, queues when needed, idempotency, retries with backoff, circuit breakers, CI/CD, observability and testing. For AI, that includes prompt versioning, automated evals (quality, factuality, safety), regression monitoring and cost control per feature and per tenant. The goal is always the same: useful, explainable and secure AI, with stable and auditable behavior.

In consulting, I apply the same discipline to real AI adoption and operations problems: process and data diagnostics, architecture and governance, prioritization by impact and risk, metrics and SLAs, and incremental rollout (MVP, pilot, scale), with no “big bang”. It can include reviewing controls and evidence trails, designing guardrails and policies, observability and enabling the team to run the solution on its own. The expected outcome is concrete: less rework and fewer exceptions, more reliability and auditability, with cost and risk under control.