Tiago Fortunato
Software 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.

• 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; 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

AI-powered career chatbot that lets recruiters and hiring managers ask questions about my background, projects, and skills — powered by a RAG pipeline over my professional knowledge base. Try it live.

• RAG pipeline with LangChain, ChromaDB, and fastembed — retrieves relevant context from a curated knowledge base before generating answers
• Streaming responses via Groq LLM (Llama 3.1) with conversation history and suggested follow-up questions
• Section-aware chunking via font-size analysis — keeps related content together for better retrieval
• Evaluation pipeline with RAGAs for measuring faithfulness, relevance, and answer quality
• FastAPI backend + vanilla JS frontend; containerized with Docker and deployed on Render

Production REST API for infrastructure inspections with autonomous AI classification. Upload a photo of road damage and the system classifies severity, damage type, and generates an explainable rationale — all 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/damage type responses
• JWT auth with admin role, per-user data isolation, status lifecycle, rate limiting
• 31 Pytest tests; 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–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 — 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