From Unstructured Documents toIntelligent Insights: An End-to-End AIPipeline for Global Tax Compliance
DOI:
https://doi.org/10.5281/zenodo.21358569Keywords:
Document AI, Tax Compliance, Large Language Models, Natural Language Processing, Electronic InvoicingAbstract
Over 100 countries now mandate electronic invoicing, with Turkey leading as an early adopter generating over two billion documents annually. For multinational enterprises, managing tax compliance across heterogeneous formats presents severe operational challenges. This study introduces an end-to-end AI pipeline that transforms unstructured invoices (PDFs, images, CSVs) into compliant data within a unified architecture.
The pipeline comprises three integrated components: First, the Smart Input Module leverages an ensemble of multimodal models (Gemini Flash, Nova Lite, GPT-4.1), achieving 98% accuracy on legally critical fields. Second, the Cloud Processing Layer utilizes a domain-specific XSLT deduplication technique to yield a 63% storage footprint reduction while preserving digital signatures, extending infrastructure runway to over five years. Supported by RabbitMQ and Kubernetes, this layer scales peak throughput from 32 to 200 transactions per second. Finally, the Sovi Intelligence Engine employs an agentic, retrieval-augmented generation (RAG) architecture to enable natural language querying across five languages, achieving a 94% valid SQL generation rate. Currently in production, this system demonstrates the viability of fully automated, AI-driven tax compliance.
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Copyright (c) 2026 Muhammed Şara, Ahmet Ak, Ayhan Boyacıoğlu, Yunus Taştutan (Author)
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