🚀 Tradu — AI Document Processing Production Line

Tradu is a production-grade, distributed AI platform designed as a digital production line for large-scale multilingual document processing.

It applies industrial-style pipeline principles to AI-powered language processing.

Developed and operated by Leonardo Martinelli.
Designed for high-throughput, fault-tolerant, and auditable AI processing in real-world production environments.

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📚 Technical Documentation

Detailed internal design, reliability model, and scaling strategy:

• 📘 Case Study — Distributed AI Processing Platform (Tradu)
• 📄 Architecture Deep Dive
• 🛡️ Reliability & Fault Tolerance
• 📈 Scalability & Capacity Engineering
• ⚙️ Operational Model
• 🔐 Security Model

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🧠 Concept: A Digital Production Line

Tradu is not a simple translation service.

It is a structured, multi-stage production system for document transformation using AI.

Each document flows through controlled stages, with quality checks, state management, and recovery mechanisms — similar to an industrial production line.

Core Principles

• Deterministic workflows
• Controlled state transitions
• Automated recovery
• Full traceability
• End-to-end observability

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📜 Project Evolution

• Phase 1 — Streamlit Monolith (2021–2022)
  Research prototype and proof-of-concept.

• Phase 2 — Containerized Platform (2023)
  First separation of services and frontend-backend decoupling. The system was packaged and published as a Docker image to enable reproducible execution.

• Phase 3 — Distributed System (2024)
  Introduction of workers, queues, and orchestration.

• Phase 4 — Production Line Platform (2025–Present)
  Fully observable and fault-tolerant architecture.

This evolution reflects continuous architectural refactoring driven by scalability, reliability, and production constraints.

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🏗️ Production Architecture

High-Level Processing Flow

Raw CSV Input
   ↓
Validation & Preprocessing
   ↓
Block Generation
   ↓
Distributed Scheduling
   ↓
AI Processing
   ↓
Quality & Consensus Layer
   ↓
Persistence & Indexing
   ↓
Incremental Delivery

Supporting Infrastructure

  Frontend → API → Orchestrator → Rotator → Redis → Bridge → Workers → AI Engine → Storage

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🔄 Production Lifecycle

Each document is decomposed into independent production units (blocks).

Each block follows a strict lifecycle:

PENDING → CLAIMED → QUEUED → PROCESSING → VALIDATING → COMPLETED / ERROR / CANCELED

The system enforces transition rules and prevents invalid state changes.

Automated watchdogs continuously monitor and recover stalled production units.

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⚙️ Production Capabilities

📁 Ingestion & Raw Material Handling

• JWT-secured uploads
• Streaming-friendly parsing
• Automatic language detection
• Persistent raw storage

🏭 Orchestration & Flow Control

• Event-driven scheduling
• Load balancing
• Backpressure management
• Worker coordination

🧠 AI Processing Line

• Multi-model inference
• TF-IDF + ST consensus
• Automatic model selection
• Fallback strategies

✅ Quality & Validation Layer

• Similarity-based validation
• Consistency checks
• Automatic retries
• Error classification

📦 Packaging & Delivery

• Block-level pagination
• Incremental result serving
• Progress monitoring APIs

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📊 Industrial-Grade Observability

Tradu integrates full production monitoring:

• Throughput metrics
• Latency per stage
• Failure rates
• Queue depth
• Service health

Using:

• Prometheus (metrics & throughput monitoring)
• Grafana (operational dashboards)
• Loki (centralized logging)
• Distributed tracing (request correlation)
• Locust (load testing & capacity planning)

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📈 Performance & Reliability

The platform has been validated under simulated production workloads using Locust:

• Sustained concurrent uploads
• Long-running translation pipelines
• Backpressure stress scenarios
• Failure injection tests
• Large-scale financial news and market datasets (institutional-grade sources)

Ensuring predictable behavior under load.

🧩 System Architecture Principles

The platform follows:

• Domain-Driven Design
• Hexagonal Architecture
• Event-driven pipelines
• Stateless service boundaries
• Explicit failure handling

This enables controlled evolution and long-term operational stability.

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🔐 Security & Production Safety

• JWT authentication
• Controlled access
• Resource isolation
• Private inference pipelines
• Auditable workflows

Core production logic remains private.

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🚀 Deployment & Scaling

• Containerized microservices
• Horizontal scaling
• Environment isolation
• Rolling updates
• Zero-downtime restarts
• Load-tested with Locust under simulated production workloads

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👤 Author

Leonardo Martinelli
Senior Data Scientist & Distributed Systems Engineer
Argentina 🇦🇷

Specialized in building AI-driven digital production systems.

Open to technical collaboration.

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📌 Disclaimer

This repository documents system architecture and production design.

Core execution logic and proprietary pipelines remain private.