Automation & AI engineer

Automation, data and AI engineering for operations that can’t afford mistakes.

From AI-generated leads to the report that lands on WhatsApp at 7:15 am: I design, build and run systems that take manual work off sales and operations, with measured numbers to back it up.

n8n workflows I authored
42
dashboards in production
13
faster ETL: from court PDFs to a spreadsheet in the CRM
8×
  • n8n
  • Power Automate
  • TypeScript
  • Python
  • FastAPI
  • React
  • Next.js
  • PostgreSQL
  • SQL Server
  • Redis
  • Docker
  • Gemini
  • LightGBM
  • Claude
  • OpenAI
  • Bitrix24
  • SharePoint
  • Power BI
  • Playwright

Automations

The SDR opens the CRM and the lead is already scored.

This is the real path of a flow in production, with generic labels. Switch to "Failure handling" and watch the backup model and the JSON fixer take over.

WebhookCRM · new leadEnrichmentPublic registry + bureauLLMScore + rationaleValidationJSON schemaCRMWrites scoreScore tableUpsert per leadBackup modelIf the primary failsJSON fixerRepairs invalid output

A CRM webhook receives the lead, it is enriched with public and credit-bureau data, an LLM returns a schema-validated JSON score, the score is written to the card and to a score table.

See all 83 automations

Work

One SaaS on the market and five systems built for real operations.

Corporate projects are anonymous, code-free and use synthetic data. The numbers come from the repositories.

ProductSaaS in production

A CRM that fits your process

Gume

New field, new pipeline, new automation. No code, no migration, no waiting on IT.

170public API operations

Metadata-driven platform: custom pipelines, visual automations, a documented public API and an MCP server for AI agents. Monorepo with web, worker and decisions recorded as ADRs.

  • TypeScript
  • Turborepo
  • PostgreSQL
  • Redis
  • MinIO
  • MCP
Corporate case

AI lead generation

B2B Prospecting Platform

Describe the market and get leads with verified records, ready for sales, straight into the CRM.

27.4kdeals trained the discard model

Outbound prospecting platform combining generative AI and cascading registry checks: the user describes a market, AI suggests the sectors, every company goes through the same registry rule and the result lands in the CRM in batches.

  • Cascading registry lookup, with the same rule at every layer.
  • Batch import with live progress, exponential retry and per-job rollback.
  • Two-phase, idempotent rescue of discarded deals.
  • A calibrated LightGBM model that learns why sales discards leads.
  • FastAPI
  • React
  • PostgreSQL
  • Gemini
  • LightGBM
Read the case study
Corporate case

Self-hosted meeting AI

Meeting Notetaker

Decisions and tasks from every meeting, with recordings and history on your own server.

3platforms: Meet, Teams and Zoom

A bot that joins Meet, Teams and Zoom, records, transcribes with speaker labels and extracts decisions and tasks. Swappable transcription and LLM providers, and full-text search across the whole history.

  • Next.js
  • Node
  • PostgreSQL
  • Playwright
  • LLM
Read the case study
Corporate case

Sales BI and governance

Sales Dashboards

One rule, from sales rep to the board: the number on the card is the same one the API returns.

13dashboards in production

CRM, SQL Server and PostgreSQL under a single business-rule layer. SSO, per-tab and per-action permissions, daily snapshots and panels for executives and office TVs.

  • FastAPI
  • SQL Server
  • PostgreSQL
  • SSO
Read the case study
Corporate case

Multimodal AI document triage

Document Robot

The AI reads, classifies and names the customer package, files it in SharePoint and flags what is missing.

94%accuracy on document type

Unzips the customer upload, identifies each document type with multimodal AI (Gemini and Claude), standardizes the name and stores it in the right SharePoint folder, with no manual triage.

  • FastAPI
  • Gemini
  • Claude
  • SharePoint
Read the case study
Corporate case

Bankruptcy filings with OCR and AI

Creditor List Extractor

88% less time reading creditor lists, with tax IDs validated and liabilities matching the printed total.

0%liability deviation (was 46%)

Combines native text, OCR and LLMs to extract creditors from PDFs, scans and spreadsheets, validates tax-ID check digits and reconciles liabilities before attaching to the CRM.

  • Python
  • OCR
  • Gemini
  • Claude
Read the case study

More work

Small tools that ended tedious tasks.

Referral API

Referral Hub

Referrals from every source in one API, no duplicates, credited to whoever referred first.

  • Unique key on the company-ID root, always keeping the oldest referral.
  • Nothing is written without --apply; rows without a valid date go to quarantine and never become "now".
  • FastAPI
  • SQLAlchemy
  • PostgreSQL
  • pandas
  • n8n

Daily pipeline alert

Stalled Deals Alert

Every day at 7:15 am, the manager gets the longest-stalled deals on WhatsApp, as a PDF.

  • Urgency bands by days stalled, highlighting deals that crossed 30 days since the last cycle.
  • A separate alert channel when PDF generation fails.
  • Python
  • Flask
  • APScheduler
  • WeasyPrint
  • n8n

Automatic executive report

C-level Report

A weekly report per executive that builds itself from a shared spreadsheet.

  • Three-step name resolution: aliases, deterministic fuzzy matching and an AI tie-breaker.
  • Whatever does not resolve goes to a review queue instead of becoming a guess.
  • Python
  • pandas
  • rapidfuzz
  • Gemini
  • Playwright

Real-time lead triage

Requalification Queue

The team decides on each lead on screen, live, with the CRM as the source of truth.

  • Every write goes through guards: resync stale data, refuse vanished items and confirm on conflict.
  • Re-reads the record after writing, because the CRM answers success even when it ignores a field.
  • Node.js
  • Express
  • SQLite
  • Socket.IO

Batch contracts

Contract Generator

One spreadsheet, one Word template, and every contract comes out as a PDF in one go.

  • Fields detected by text color in the template, with no special syntax for whoever writes the contract.
  • Batch conversion with a single Word instance.
  • Python
  • python-docx
  • openpyxl
  • pywin32

How I work

Built to keep working at 3 a.m.

Measure before claiming

Every automation starts with a baseline and ends with evidence: logs, counts and dry-runs before writing to production.

Failure is a requirement

Retry only when safe, idempotency, reprocessing queues and alerts. The question is what happens at 3 a.m.

Smallest thing that works

I reuse what exists, avoid needless layers and ship in short cycles with real validation.

AI with a contract

LLMs return schema-validated JSON, with model fallback and auditable output.

What does your team still do by hand?

Describe the process. I will reply with a path to automate it and how to measure the result.

[email protected] · +55 64 9 3300-4125