Matheus Silveira
Applied AI Engineer · Automation Systems Builder
Building reliable AI-powered workflows and internal systems for real business operations.
I design and implement AI-powered workflows that connect LLMs, APIs, business data, validation layers, human review, and operational reporting across GTM, RevOps, customer operations, and internal processes.
Beyond automation. Reliable AI systems.
Most AI automation breaks when it reaches real operations: messy inputs, edge cases, approvals, customer replies, retries, missing data, and unclear ownership. My work focuses on the system around the model — the workflow logic, state, safeguards, integrations, and handoff patterns that make AI useful in production.
AI as a system component
LLMs are used for classification, drafting, enrichment, and reasoning — not as uncontrolled decision-makers.
Workflow state over one-off tasks
Each system tracks records, statuses, approvals, replies, and handoffs across multiple steps.
Human control where it matters
Sensitive actions can require review, approval, edit, rejection, or escalation before execution.
Reliability by design
Logs, fallback paths, error alerts, validation layers, and deterministic routing are built into the workflow.
Selected work
Production AI systems built for real operations — sanitized for sharing.
Each case study focuses on a different business system, but the pattern is the same: AI assists, workflow state controls, humans approve sensitive actions, and logs make the system observable.
How to read these case studies
These are sanitized examples of real systems and workflow patterns I have designed or implemented. Screenshots and details may be edited to protect client data, but the architecture, system logic, tools, and operational patterns reflect real work.
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.
— AI is one step in the system — not the whole system.
The stack I ship with
Tooling chosen for reliability, observability, and operator-friendly handoff.
Where I fit
I work best with teams that need to turn messy business processes into reliable AI-powered workflows — especially across operations, GTM, RevOps, customer communication, reporting, and internal systems.
I have built production workflows for customer communication, e-commerce, lead generation, reporting, internal operations, and AI-assisted decision support. My strength is translating real business processes into reliable systems using APIs, LLMs, data validation, and automation architecture.