vitor pereira

Applied AI in real systems.

I design and deploy agents that execute real workflows inside the tools your company already uses — with logs, rules, human approval, and documentation.

finance-agentsimulated
  1. 09:14:02 — invoice received by email
  2. 09:14:03 — amounts checked against the order
  3. 09:14:03 — R$ 312.00 mismatch on shipping
  4. 09:14:04 — waiting for approval → Ana (finance)
  5. 09:16:40 — approved by Ana — entry posted
example run

in production

simultaneous founders
70+simultaneous founderson a live conversational agent, with its own safety rules
AI analyses / day
~700AI analyses / dayautomatic summaries generated every 3 minutes
interactions processed
3.6M+interactions processedacross ~2,900 WhatsApp groups, with AI built into the product
production releases
~400production releaseson a platform with 165 passing automated tests

how it works

How an agent works inside your process

  1. 01

    Something arrives

    A message, an email, a file, or an order.

  2. 02

    The agent understands it

    It reads, extracts what matters, and checks it against the process rules.

  3. 03

    It acts in your tools

    The ones your company already uses. No need to switch systems.

  4. 04

    You approve what is sensitive

    Anything that can't be undone waits for a person to decide.

  5. 05

    Everything is recorded

    You can see what was done, when, and why.

example

The same process, before and after

Today

  • Someone copies data from one system into another.
  • Checks are done by hand, item by item.
  • Errors show up late, after they turned into rework.
  • Nobody knows exactly what was done and when.

With an agent

  • Repetitive work runs on its own, as it arrives.
  • The agent stops and asks when a case is sensitive.
  • Mismatches show up right away, with the reason.
  • Every step is recorded and can be reviewed.

What I do

Agents that execute

An agent that does the work — not one that only answers questions.

Process automation

Integrations and routines that run on their own across your company's tools.

AI inside your product

An assistant that answers about each customer's data, inside the system.

Security and control

Each agent only reaches what it needs, and the rule lives on the server (not in the prompt text).

Featured projects

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clearseg.com.br

ClearSeg

Ongoing

2025 · Own product · Co-founder & CTO

AI-powered recurring billing for insurance brokers — from file to reconciliation.

  • AI invoice reading (classify, extract, detect duplicates)
  • On-time recurring billing under the broker's brand
  • White-label policyholder portal + book-of-business dashboard
  • IA / OCR
  • Next.js
  • Node.js
  • PostgreSQL
  • Automação
Visit project
sarcorps.com.br

SARCORPS

Ongoing

2025 · Co-founder · Full-stack co-lead

Specialized Search and Rescue (SAR) technology for aviation and defense.

  • SAR platform on the international IAMSAR standard
  • Multi-tenant security: RLS / SECURITY DEFINER, JWT, PII sanitization
  • pt/en/es i18n with custom tooling + accessibility (WCAG)
  • Nuxt / Vue
  • TypeScript
  • Supabase
  • RLS
  • Python
Visit project
aihub.pixeleducacao.com.br

Pixel AI Hub

Ongoing

2026 · Pixel Educação · Software Engineer

AI agents that work for your business 24/7, no technical team required.

  • ~400 production deploys with a 165-test suite green
  • Live agent for 70+ simultaneous founders (2,411 messages in 123 sessions)
  • Fleet of 19 agents as code + supervisor across 9 servers
  • Node.js
  • TypeScript
  • PostgreSQL
  • n8n
  • WhatsApp API
Visit project
mygroupmetrics.com

My Group Metrics

Completed

2024–2026 · My Group Metrics · Principal Engineer

In-product analytics and AI for WhatsApp communities at scale.

  • 3.6M+ interactions processed across ~2,900 groups
  • ~700 automatic summaries per day (audio + LLM, every 3 min)
  • AI Copilot: natural-language Q&A over the client's data
  • Next.js
  • Node.js
  • OpenAI
  • n8n
  • PostgreSQL
Visit project

evidence before claims

Production cases and decisions

These are not polished demos. They cover failures, boundaries, and decisions that appear only after a system starts operating for real.

Latest posts

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Frequently asked questions

Does this replace my team?

No. The agent takes over the repetitive part of one bounded workflow; a person stays accountable for the outcome and approves high-impact actions. If the goal is to replace a whole team on day one, this is not the right place to start.

What if the AI gets it wrong?

The agent only gets the permissions it needs, fails visibly, and asks for a human decision when an action is irreversible or outside its contract. Its actions are logged, so you can see what happened and fix it.

Do I need to change the systems I use?

No. The agent works inside the tools your company already uses, as long as they can be accessed through an API.

How long does it take and how much does it cost?

A pilot for one workflow takes 21 to 30 days, from R$ 20,000. The exact number comes out of the scoping session, which is included. The first conversation, the AI Automation Diagnostic (30 min), is free.

Is my data safe?

The boundary lives on the server, not in the prompt: the agent only reaches the data allowed for that workflow, and high-impact actions go through human approval and are logged.

Do you have a repetitive workflow that keeps breaking?

Show me the current flow, the tools involved, and where work gets stuck. We start by deciding whether an agent helps or merely adds complexity.

Free, no strings attached.