Matheus Silveira
AI Automation Engineer · Applied AI Systems Builder
Building reliable AI-powered workflows and internal systems for real business operations.
I design and implement LLM workflows, API integrations, RAG-ready systems, structured outputs, validation layers, human review loops, and operational reporting across GTM, RevOps, customer operations, and internal processes.
Selected work
Production AI systems built for real operations — sanitized for sharing.
Local Handyman AI Intake & Scheduling System
AI-powered SMS intake, repair photo analysis, lead routing, and scheduling workflow for a home services operation.
DTC Revenue Automation & Workflow Reliability
Revenue automation system for post-purchase, upsell, cart recovery, reporting, and workflow reliability across e-commerce operations.
AI GTM & Sales Agent Pipeline
Lead sourcing, enrichment, personalization, outbound automation, reply classification, and CRM routing using AI workflows.
A repeatable approach to applied AI
Every system I ship runs through the same engineering discipline.
Map the business process
Start with the real workflow — actors, triggers, decisions, edge cases — before any tool gets opened.
Normalize and validate data
Clean inputs, enforce schemas, and validate everything that crosses a system boundary.
Connect APIs and events
Wire systems together with webhooks, queues, and idempotent calls — built to handle retries.
Add LLM reasoning where useful
Use models for classification, extraction, summarization, and decisions humans currently make manually.
Structured outputs and guardrails
JSON schemas, validators, and fallbacks so the LLM produces data the system can actually trust.
Human review where risk exists
Approval steps, escalation paths, and audit trails for any action with real-world consequences.
Log, monitor, and improve
Observability into every run — errors, latency, costs, and edge cases — feeding continuous iteration.
Connect results to business metrics
Tie workflow outputs back to revenue, response time, throughput, or whatever the operation actually cares about.
The stack I ship with
Tooling chosen for reliability, observability, and operator-friendly handoff.
Who I work with
Matheus combines business automation, growth and revenue operations, and applied AI engineering.
He has built production workflows for customer communication, e-commerce, lead generation, reporting, internal operations, and AI-assisted decision support. His strength is translating real business processes into reliable systems using APIs, LLMs, data validation, and automation architecture.
Want to build reliable AI systems for business operations?
Tell me about the workflow you want to automate or the system you want to make trustworthy.