Projects

HTTP service that reads and decodes QR codes from Portuguese fiscal documents (PDFs), returning structured JSON with issuer tax IDs, buyer information, document types, VAT lines, and totals. No PDF files are stored on the server — files are written to temporary OS storage, processed, and immediately deleted. Live Prodution Try it online — no account needed: qrcode.appa8.com Features PDF upload via HTTP multipart QR code detection across all pages ATCUD validation per AT specification Raw scan endpoint (/scan) and structured parse endpoint (/parse) Embedded Portuguese web interface Interactive OpenAPI 3.1 / Swagger documentation Docker and Portainer ready API Endpoints Endpoint Description POST /api/v1/document/scan Raw QR code content POST /api/v1/document/parse Structured fiscal data GET /api/v1/version Service metadata GET /health Health check GET /docs Swagger documentation Tech Stack Go Gin (HTTP framework) Huma v2 (OpenAPI/Swagger) gozxing (QR detection) poppler-utils / pdftoppm (PDF rendering) HTML + Tailwind CSS + Vanilla JS (frontend) Docker / Docker Compose Architecture Follows a simplified Domain-Driven Design with separate layers for configuration, domain entities, application services, infrastructure adapters, HTTP interfaces, and embedded UI. ...

AI-powered email automation and classification engine, part of the Appa8 AI Process Automation Hub. MailFlow ingests emails from IMAP or Microsoft 365, classifies them using a hybrid rule + LLM approach, automatically routes them to the correct folders, and provides full supervision via a web dashboard. Designed for on-premise deployments where data privacy is critical. Features IMAP and Microsoft Graph / Outlook ingestion RFC822 email parsing with attachment handling Hybrid classification: rule-based matching + local LLM validation (Qwen 2.5) Auto-move to classified folders PDF export with customisable path templates Invoice worker: PDF QR extraction and payment data parsing Learning Mode: human review and approval before filing Natural language search via Telegram (“send invoices from vendor X in January”) Encrypted credential storage (Fernet) Redis job queues with AOF persistence PostgreSQL persistence (async SQLAlchemy 2.0) Streamlit supervision dashboard Docker-based deployment with Traefik reverse proxy CI/CD via GitHub Actions + Portainer auto-deployment Architecture Email Inbox (IMAP / Microsoft 365) │ ▼ email-worker (poll, parse, route) │ ▼ Redis Queue │ ┌──────┴───────┐ ▼ ▼ ai-worker invoice-worker (LLM classify) (QR + finance) │ ┌────┴────────────────────┐ ▼ ▼ ▼ folder review-worker query-worker move (Learning Mode) (NL search) │ telegram-bot (approvals + search) Worker Description email-worker Polls IMAP / M365, routes by worker type ai-worker Main classification engine with Redis job processing invoice-worker Financial document extraction from PDF QR codes review-worker Learning Mode human review cards query-worker Natural language search processing telegram-bot User interface for commands and approvals Dashboard The Streamlit interface provides multi-tab supervision: ...

🏗️ Equipment Gest Assets A logistics, maintenance, and chain-of-custody tracking system for industrial and construction assets. 📋 Overview Equipment Gest Assets is designed to eliminate the “black hole” of equipment tracking in the construction and industrial sectors. It provides a centralized source of truth for every tool and machine in your fleet, ensuring managers know exactly where an asset is, who is responsible for it, and what its current operational health is. ...