Synopsis
Over 60 countries now mandate electronic invoicing, and this number has since exceeded 100 countries. Turkey is among the earliest adopters, with the Turkish Revenue Administration (GİB)
generating more than 2 billion documents annually. For enterprises operating across multiple jurisdictions, meeting these obligations creates serious operational and legal challenges that become increasingly difficult to manage as transaction volumes increase. Traditional rule-based approaches were not built for this scale; they struggle with heterogeneous input formats and cannot easily absorb changes in jurisdiction-specific tax rules. This study presents an end-to-end artificial intelligence pipeline that addresses these challenges by transforming unstructured invoice documents (PDFs, images, and CSV files) into actionable compliance insights within a single, unified architecture.
The pipeline integrates three tightly coupled components. The Smart Input Module uses a competitive ensemble of large multimodal models: Google Gemini Flash, Amazon Nova Lite, and OpenAI GPT-4.1 to extract tax-relevant fields from diverse document formats, achieving 92% overall extraction accuracy and 98% accuracy on legally critical fields, such as tax identification numbers and monetary amounts. The Cloud Processing Layer introduces a domain-specific XSLT deduplication technique that exploits a structural regularity unique to Turkish e-invoices: embedded rendering stylesheets that are nearly identical across documents from the same taxpayer. By replacing duplicate stylesheets with compact SHA-256 hash references while reinserting exact original bytes during reconstruction to preserve digital signature integrity, the system achieves 90% compression on document content, contributing to a 63% reduction in total storage footprint and extending the infrastructure runway from 18 months to over five years. An event-driven architecture built on RabbitMQ and Kubernetes autoscaling raises the peak throughput from 32 to 200 transactions per second, a 6.25× improvement. Redis caching further reduced the taxpayer lookup latency from 10 ms to 0.9 μs. The Sovi Intelligence
Engine completes the pipeline by enabling natural language querying of processed tax data in Turkish, English, German, French, and Spanish. Based on an agentic architecture, Sovi decomposes each query into sub-steps: intent interpretation, schema mapping, and SQL generation, guided by a retrieval-augmented generation workflow that maps domain terminology to database columns. In the evaluation, the system achieved a 94% valid SQL generation rate and 82% semantic accuracy, with a mean response latency of 3.2 s and a user satisfaction score of 4.1 out of 5. Running in production today, the pipeline demonstrates that fully automated, AI-driven tax compliance is not a future aspiration but a current reality in Turkey.
