Tiago Fortunato
Product Engineer

WhatsApp-first scheduling SaaS for Brazilian freelance professionals (therapists, personal trainers, salons). Clients book online, receive automatic WhatsApp reminders 24h before, and professionals manage everything from a dashboard. Live product at odys.com.br, currently in active validation with industry professionals before scaling marketing.

• WhatsApp-native reminders via self-hosted Evolution API v2 on Railway, triggered by Supabase pg_cron (not a third-party bot)
• Multi-tenant architecture: each professional gets a public booking page at /p/[slug] with fully isolated data and configurable availability
• Stripe subscriptions + PIX with webhook handling; Supabase Auth; Drizzle ORM on PostgreSQL; resolved PgBouncer transaction mode compatibility
• Rate limiting with Upstash Redis; error monitoring with Sentry; transactional email via Resend
• Deployed on Vercel (app) + Railway (WhatsApp API Docker container); CI/CD via GitHub Actions

Production RAG chatbot where recruiters and hiring managers ask questions about my background, projects, and skills, and get answers grounded in a curated knowledge base. Self-hosted on AWS EC2.

• Hybrid retrieval: semantic search + BM25 fused with Reciprocal Rank Fusion (RRF) for higher precision than either method alone
• Custom section-aware chunking via font-size analysis: keeps semantically related content together for better retrieval quality
• Streaming SSE responses via Groq (Llama 3.1) with conversation history and suggested follow-up questions
• RAGAs evaluation pipeline measuring faithfulness, answer relevance, context precision, and context recall
• FastAPI backend with LangChain and ChromaDB vector store; vanilla JS frontend; containerized with Docker
• Self-hosted on AWS EC2 with Nginx reverse proxy and Let's Encrypt HTTPS at chatbot.tifortunato.com

Production REST API for infrastructure inspections with autonomous Vision AI classification. Upload a photo of road damage and the system classifies severity, damage type, and generates an explainable rationale, all running in the background.

• Vision AI classification via Groq SDK: images are compressed client-side, sent to the API, and classified autonomously using background tasks
• Explainable AI (XAI): every classification includes a human-readable rationale field so decisions are transparent and auditable
• LangSmith integration for full LLM observability: trace every prompt, response, and latency in production
• Structured LLM output enforcement with LangChain for consistent severity and damage type responses
• JWT auth with admin role, per-user data isolation, status lifecycle, rate limiting
• Comprehensive Pytest suite covering all endpoints; CI/CD via GitHub Actions; Docker; frontend dashboard on Vercel with image upload and AI status badges

Two-stage hybrid pipeline combining YOLOv8-based object detection with a rule-based expert system to detect road surface damage and automatically prioritize maintenance actions. Trained and evaluated on the RDD2022 benchmark dataset.

• Conducted 4 controlled experiments (EXP1 to EXP4) varying model capacity, input resolution, and training length. Best config (YOLOv8s): mAP50 0.663, Precision 0.694, Recall 0.604
• Built a rule-based expert system with geometric filtering, continuous severity scoring, and quantile-based prioritization (LOW / MEDIUM / HIGH)
• Post-processing reduced noisy detections by 31.2% (8304 → 5711) while preserving structurally relevant defects
• Designed for interpretability: every prioritization decision traceable to explicit rules, with no black boxes

End-to-end ML pipeline analyzing vehicle sensor data (temperature, vibration, oil pressure, RPM) to predict failures before they occur.

• Multi-stage pipeline: data generation → ETL → EDA → ML → dashboard
• Random Forest classifier with imbalanced data handling (99.9% accuracy)
• Live Streamlit dashboard for real-time vehicle condition monitoring

Recommendation engine using matrix multiplication and Python multithreading to simulate scalable user-product scoring, with performance benchmarking between sequential and parallel execution.

• Implemented S = U × P matrix scoring with score normalization
• Compared sequential vs. multithreaded performance with visual output
• Demonstrated threading tradeoffs: overhead vs. scalability at scale