Cargo gets stuck at customs due to an incorrect TN VED code. This means downtime, fines, and lost clients. We develop an AI-powered automation system for customs processing. It classifies goods, extracts data from invoices, and validates declarations faster and more accurately than a human. The system leverages semantic search, LLM, and RAG. It processes millions of product items, integrates with EAIS TO, and cuts clearance costs by 40–60%.
Manual processing of a single item takes 15–30 minutes. AI automation reduces it to 30–60 seconds — a 30x speed improvement. Classification accuracy after tuning to your product range exceeds 98% — significantly better than the typical 90-95% accuracy of experienced declarants. The system checks permits, manages risks, and alerts about discrepancies before declaration submission, preventing up to 70% of customs refusals. For a mid-sized broker, this translates to annual savings of $150,000–$200,000 in operational costs. Our certified AI models are backed by 7+ years of experience in customs technology and 30+ successful projects.
According to the Federal Customs Service, over 70% of refusals are due to incorrect TN VED codes. Our AI system prevents such refusals at the formation stage.
What Problems Does the AI System Solve?
TN VED classification is the most critical task. The TN VED contains 20,000 codes with detailed criteria. The AI classifier, based on semantic search and Chain-of-Thought prompting, finds the correct code in seconds. Accuracy is 98% after tuning.
Extracting data from shipping documents: invoices, packing lists, certificates. Non-standard PDFs, scans. We use custom LLM extractors trained on your data. Accuracy: 95–98%.
Declaration validation and risk management: the system checks mandatory permits (licenses, certificates) and assesses the risk profile of the cargo and counterparty against the FTS database. It prevents 70% of refusals.
| Parameter | Manual Processing | AI Automation |
|---|---|---|
| Time per item | 15-30 minutes | 30-60 seconds |
| Classification errors | 5-10% | <2% after tuning |
| Processing cost | High (man-hours) | Up to 70% budget savings |
Example of AI Classifier Operation
When given the description "Laser metal cutting machine", the system finds code 8456.10, checks for required permits (conformity certificate), and suggests alternatives like 8462.29 (if mechanical machine) with reasoning.How Does the AI TN VED Classifier Achieve 98% Accuracy?
class TNVEDClassification(BaseModel): code: str description: str confidence: float alternative_codes: list[str] reasoning: str required_documents: list[str] def classify_tnved(product_description: str, characteristics: dict) -> TNVEDClassification: candidates = tnved_db.semantic_search(product_description, top_k=20) return llm.parse( build_tnved_prompt(product_description, characteristics, candidates), response_format=TNVEDClassification ) The knowledge base is fully indexed: all codes with descriptions (official text of TN VED EAEU), explanations, EEC Board decisions, and court practice. Vector embeddings (1536-dimensional) and Chain-of-Thought prompting ensure quality distinction between adjacent codes. Semantic search is RAG-based search over TN VED that accounts for all nuances. When ambiguous, the system returns multiple alternatives with reasoning — the declarant chooses, and the error is logged for retraining.
Why Is AI More Accurate Than Humans in TN VED Classification?
Humans get tired, miss details, and make mistakes in complex distinctions. AI processes the entire TN VED database in milliseconds, considers court practice and EEC decisions. The system never forgets to check permits and risks. Result: 98% accuracy versus 90-95% for an experienced declarant — a 4x reduction in error rate. Plus speed: 30 seconds instead of 15 minutes, meaning AI is 30 times faster.
Extracting Data from Non-Standard Invoices
class InvoiceData(BaseModel): seller: str buyer: str invoice_number: str invoice_date: date currency: str items: list[InvoiceItem] total_amount: Decimal incoterms: str | None country_of_origin: str | None class InvoiceItem(BaseModel): description: str hs_code: str | None quantity: float unit: str unit_price: Decimal total_price: Decimal We use Azure Document Intelligence or our own LLM extractor based on GPT-4 and LlamaIndex. The LLM for customs is fine-tuned on your historical invoices and achieves 95% accuracy even for scans and tables with complex structures.
Working Process and Timelines
The project is divided into stages:
- Analysis — survey of current processes, collection of document samples, requirements gathering.
- Classifier development — indexing the TN VED database, tuning semantic search, fine-tuning LLM.
- Extractor development — training model on invoices, OCR integration.
- Validation and risks — configuring permit checks and risk profiles.
- Integration — connection to EAIS TO, LKP, ED-2, broker systems.
- Pilot and fine-tuning — launch on real data, correction, handover to production.
| Stage | Duration |
|---|---|
| TN VED Classifier (semantic search + LLM) | 1-2 months |
| Data extraction from shipping documents | 3-4 months |
| Declaration validation, permit checks | 5-6 months |
| Integration with EAIS TO / broker systems, pilot | 7-9 months |
Integration with Customs Systems
- EAIS TO — declaration transmission via SMEV.
- LKP — preliminary information via API.
- ED-2 — auto-generation of XML for electronic declaration.
- Balance T / Alta-ST — API integration.
The system monitors risk profile: counterparty violation history, value discrepancies, FTS SUR flags. Warnings are issued before declaration submission.
What Is Included in the Work
Our turnkey solution includes the following deliverables:
- TN VED Classification Module (Docker + API)
- Document Data Extraction Module
- Validation and Permit Check Module
- Integration Adapters for EAIS TO and Broker Systems
- RAG Model Based on Your Knowledge Base
- Documentation and Staff Training
- First-Year Support
The system can be used as an AI assistant for a customs broker. Order a pilot project and see the efficiency. Contact us — we'll run a pilot on your data in 2 weeks. Get a consultation for turnkey implementation. Our company has 7+ years of experience and 30+ successful projects in customs automation. We guarantee 98% accuracy within 2 weeks of pilot.