Documentary on AI: How Artificial Intelligence Is Transforming Logistics and International Trade

A commercial invoice lists a weight of 12,500 kg. The bill of lading lists 12,850 kg. The packing list lists 12,600 kg.

The three documents were read correctly. The data was extracted. However, the file contains an inconsistency that could lead to an operational error, a request for clarification, or a delay.

This example illustrates one of the main challenges ofdocument-based AI in logistics and international trade: simply understanding each document individually is not enough.

An international shipment may involve a wide variety of documents: shipping orders, commercial invoices, packing lists, bills of lading, CMRs, AWBs, certificates of origin, customs declarations, freight invoices, proofs of delivery, and regulatory documents.

This information is shared via email, as PDF files, or through partner portals, and must then be fed into ERP, TMS, WMS, customs software, and financial systems.

For a long time, document automation focused on a single goal: reading documents and extracting data from them. Advances in artificial intelligence now make it possible to go a step further.

The challenge is no longer just about knowing what a document contains. It also involves understanding the relationships between multiple documents, cross-referencing their information, detecting inconsistencies, and verifying the completeness of a file.

It is this evolution that is currently transforming document-based AI as applied to logistics and international trade.

What is documentary AI?

Documentary AI refers to the use of artificial intelligence technologies to read, understand, extract, structure, and verify the information contained in documents.

In logistics and international trade, she can handle the following, among other things:

• shipping orders;
• commercial invoices;
• packing lists;
• Bills of Lading;
• CMRs and AWBs;
• certificates of origin;
• customs documents;
• shipping invoices;
• proof of delivery;
• regulatory documents.

Its goal is to transform unstructured information into data that can be used by operational teams and their business systems.

Document-based AI can thus automatically identify a document type, extract relevant information from it, structure the data, and then transmit it to an ERP, a TMS, a WMS, or another operational tool.

But in international operations, an additional challenge arises: useful information is rarely contained in a single document.

A commercial invoice may list one quantity. The packing list lists another. The bill of lading provides information on weight and transportation. A certificate specifies the origin of the goods. A customs declaration contains yet more information about the same transaction.

Documentary AI must therefore address several levels of complexity:

Read. Extract. Organize. Compare. Verify.

The shift from analyzing a single document to understanding an entire set of documents is now one of the main challenges facing artificial intelligence as applied to logistics.

AI, Machine Learning, and Deep Learning: What Technologies Lie Behind Document-Based AI?

Document-based AI draws on several generations of technologies that have gradually improved machines' ability to process documents.

The terms “artificial intelligence,” “machine learning,” and “deep learning” are often used interchangeably. However, they refer to distinct concepts.

Artificial Intelligence: Automating Cognitive Tasks

Artificial intelligence is the broadest field. It encompasses technologies that enable computer systems to perform tasks that typically require certain human abilities: recognizing, interpreting, predicting, recommending, or handling part of a process.

In the field of document processing, this may involve automatically identifying the type of document, understanding certain information, or detecting an anomaly.

Machine Learning: Learning from Examples

Machine learning is a branch of artificial intelligence. Instead of manually programming all possible rules, a model learns from examples.

When applied to documents, machine learning makes it possible, in particular, to:

• distinguish between an invoice and a packing list;
• automatically classify documents;
• recognize certain fields;
• identify recurring information;
• improve predictions based on training data.

This approach marked a significant shift from systems based entirely on rigid rules and templates.

Deep Learning and Neural Networks: Processing More Complex Documents

Deep learning is a subcategory of machine learning based on neural networks consisting of multiple processing layers.

These technologies have significantly improved the systems' ability to process:

• the text;
• the images;
• the tables;
• the different layouts;
• multilingual documents;
• heterogeneous document formats.

In document processing, these advances have gradually made it possible to move beyond simple character recognition to better identify the structure and meaning of information.

But to understand today's documentary AI, we need to take a closer look at the evolution of the technologies that preceded it.

From OCR to AI Agents: How Document-Based AI Has Evolved

Document automation did not begin with generative AI. It is the result of several decades of technological evolution.

Each generation has helped solve another part of the problem:

OCR → Machine Learning → IDP → Generative AI → AI agents.

OCR: Reading the characters in a document

OCR, or optical character recognition, converts an image containing text into digital text that can be processed by a computer system.

For example, this technology can recognize:

• a container number;
• an address;
• a date;
• an amount;
• a reference number on a scanned invoice.

But recognizing a sequence of characters does not necessarily mean understanding its purpose.

A system may read "€347.50" without knowing whether it is a total amount, a shipping cost, a tax, or some other financial item.

OCR reads the text. It does not necessarily understand the document.

Machine Learning: Classifying and Extracting Information

Machine learning has enabled document management technologies to learn how to recognize different types of documents and identify relevant information.

A system can thus automatically distinguish between:

• a commercial invoice;
• a packing list;
• a bill of lading;
• a CMR;
• a certificate;
• a shipping invoice.

It can then extract certain data and submit uncertain cases for human validation.

This " human-in-the-loop " approach allows an operator to intervene when an extraction is ambiguous or when a decision requires specialized expertise.

IDP: Understanding and Organizing Documentary Information

Intelligent Document Processing, or IDP, expands these capabilities by combining, among other things, document classification, data extraction, data structuring, and validation.

For example, the system can determine that a field matches:

• to the recipient;
• on an issue date;
• to a total amount;
• with a gross weight of;
• to an invoice number;
• to a product reference.

These capabilities remain essential. But they primarily answer one question:

What is in this document?

In logistics and international trade, another issue is quickly becoming essential:

Is the information contained in this document consistent with the rest of the file?

This is where the limitations of an approach focused exclusively on extraction become apparent.

Generative AI and Foundation Models: Understanding a Wider Variety of Documents

The advent of foundation models and generative AI has improved systems' ability to understand a variety of content without relying solely on predefined templates.

Modern models can process the following simultaneously:

• of the text;
• paintings;
• images;
• complex structures;
• various formulations.

This multimodal capability makes it possible to process a wider variety of documents and interact with their content using natural language.

The models can thus identify information in documents whose structure varies significantly from one supplier, carrier, or country to another.

But one limitation remains:

Understanding a document does not mean understanding a process.

A model can accurately extract a weight of 12,500 kg from a commercial invoice. Without additional context or a verification mechanism, it does not necessarily know that the bill of lading associated with the same record lists 12,850 kg.

It is precisely at this point that the documentary context takes center stage.

AI Agents: From Understanding to Orchestration

A new trend is emerging with AI agents and agent-based workflows.

Instead of performing a single, isolated task, an agent can carry out several steps in a process in sequence:

• read a document;
• extract the data;
• query a business system;
• compare several sources;
• detect an anomaly;
• recommend an action;
• to feed into an operational tool.

In a logistics process, this might involve, for example, reading an invoice, reviewing the data available in a TMS, reconciling it with a shipping document, and then flagging any discrepancies.

But the more capable a system is of taking action, the more important traceability, auditability, and human oversight become.

An extraction error can be corrected. An incorrect action that has already been performed in a business system may be more difficult to undo.

The goal, therefore, is not to pursue autonomy at any cost, but to build systems capable of reasoning and acting within a controlled environment.

Why Logistics and International Trade Pose a Unique Challenge for Document-Based AI

Not all document management processes are equally complex.

In logistics and international trade, three factors make automation particularly challenging: the sheer volume of documents, the fragmentation of data, and the constantly changing nature of files.

A transaction involves many interdependent documents

An international shipment may involve a shipping order, a commercial invoice, a packing list, a bill of lading, a CMR or an AWB, a certificate of origin, health certificates, licenses, a customs declaration, a freight invoice, and proof of delivery.

Each document provides a portion of the information. None of them, on its own, necessarily reflects the full picture of the operation.

A commercial invoice may be perfectly valid. A bill of lading may also be filled out correctly. However, both may contain conflicting information.

It is precisely in the relationships between these documents that many anomalies arise.

Information is scattered across numerous systems

Data may also be scattered across:

• emails;
• PDF attachments;
• MSDs;
• WMSs;
• public buildings;
• customs platforms;
• carrier portals;
• financial tools;
• internal guidelines.

Teams must then search for, re-enter, and compare information spread across multiple environments.

Documentary AI can help link these sources and transform unstructured information into data that can be used in business processes.

Portfolios are built up gradually

A logistics file almost never arrives complete in one go.

A first email is received. Then an invoice. A shipping document arrives next. A new version is sent. A certificate is added. A correction is made. Approval is granted.

The case is constantly evolving.

To understand an operation, a system must therefore be able to maintain its context and enrich it as new information becomes available.

The real problem: errors often occur between documents

For a long time, the performance of document management solutions was primarily evaluated based on their ability to accurately extract data.

This capability remains essential. However, proper extraction does not guarantee that a file is reliable.

Let's look at a few examples:

• the weight listed on the commercial invoice differs from that on the bill of lading;
• The quantity on the packing list does not match the quantity on the invoice;
• The country of origin varies depending on the supporting documents;
• The HS code appears to be inconsistent with the description of the goods;
• A required regulatory document is missing from the file;
• A shipping invoice does not comply with the negotiated rate terms.

In all these cases, the problem does not necessarily lie within a single document. It arises between multiple sources of information.

That is precisely the role of document reconciliation : automatically comparing information from multiple documents, systems, or repositories to identify inconsistencies, discrepancies, and missing elements.

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A company doesn't just need extracted data. It must be able to determine whether it can trust that data before using it in its operations.

From a Single Document to a Complete File: Changing the Framework for Reasoning

A logistics operator doesn't actually work on a single document. They work on a file.

A customs declarant does not verify a commercial invoice separately from the other documents.

A freight forwarder does not issue a bill of lading without taking into account the purchase order, the packing list, and the other shipping documents.

A compliance officer must understand the relationships between various supporting documents, versions, and sources.

However, historically, much of documentary technology has continued to treat the document as the primary unit of analysis.

Real change involves making the shift from:

from the individual document → to the relationships between documents → to the complete file → to the persistent operational context.

The file then becomes the unit of reasoning.

Each new document enhances our understanding of the operation. Each check improves its reliability. Each validation or correction provides additional context.

That is the logic behind the Living Dossier: a dynamic, evolving documentary representation of a business process.

Unlike a static folder, which simply stores files, a Living Dossier evolves as the process unfolds. New documents are added, new versions are incorporated, checks are triggered, and validations gradually enrich the context.

From Data Extraction to Reconciliation: How Document-Based AI Ensures the Accuracy of a Logistics File

To move from processing a single document to understanding an entire file, several steps are required.

1. Import documents from various sources

Documents may come from emails, attachments, portals, ERP systems, TMS systems, partner platforms, or customs systems.

The goal is to gather the information needed to understand the operation.

2. Automatically file documents

The system identifies the type of each document: commercial invoice, packing list, bill of lading, CMR, AWB, certificate, freight invoice, or customs document.

This classification then makes it possible to apply the appropriate treatments and controls.

3. Extract and organize the data

The information contained in the documents is identified and converted into actionable data:

• quantities;
• weight;
• amounts;
• product references;
• country of origin;
• customs codes;
• dates;
• shipping information.

4. Consolidate the information at the file level

Data extracted from several documents is compiled to provide a consolidated view of the operation.

The system no longer processes documents one by one. It begins to process them at the file level.

5. Reconcile the various sources

Information from multiple documents or systems is automatically compared.

For example:

• Weight on the invoice vs. the bill of lading;
• Quantity on the invoice vs. the packing list;
• Declared origin vs. supporting documents;
• Amount billed vs. negotiated terms.

The goal is no longer simply to extract more data, but to determine whether the available information is consistent with one another.

6. Detect anomalies and missing documents

The system can flag conflicting information, a missing document, potentially non-compliant data, or an item requiring human validation.

This allows teams to focus their attention on cases that truly require their expertise.

7. Recommend and support the initiative

Depending on the business process, document-based AI can help:

• report a problem;
• request a verification;
• identify a missing part;
• support a HS code recommendation;
• pre-fill certain information;
• feed data into a business system.

The goal is not necessarily to replace human decision-making, but to provide teams with more reliable information and a more comprehensive context for making decisions.

Key Use Cases for Document-Based AI in Logistics and International Trade

Documentary AI can be used at various stages of operations.

Automate Data Entry in TMS, WMS, and ERP Systems

A significant portion of administrative work still involves manually copying information from emails, PDFs, or attachments into business systems.

Document-based AI can extract data from a shipping order, invoice, packing list, or bill of lading to feed into a TMS, ERP, or other system.

This automation reduces the need for re-entry and allows teams to devote more time to exceptions and higher-value tasks.

Reconciling multiple documents for the same transaction

A transaction may contain multiple documents describing the same goods or the same shipment.

Documentary AI can automatically compare commercial invoices, packing lists, bills of lading, customs documents, and other sources in the file.

This reconciliation makes it possible to identify discrepancies before they become operational incidents.

Detect inconsistencies before they cause delays

A discrepancy in weight, quantity, reference, or origin may result in requests for clarification, corrections, or delays.

Detecting these anomalies early enough allows teams to take action before the case moves further through the process.

Documentary AI thus shifts from a reactive approach to a proactive one.

Ensuring Compliance with Customs Documentation Requirements

Customs procedures require documentation that is consistent, complete, and in compliance with applicable requirements.

Specialized documentary AI can assist teams in:

• detect missing documents;
• compare information across multiple documents;
• identify inconsistencies regarding countries of origin;
• report discrepancies between financial data and reported information;
• Determine the appropriate HS code recommendations based on the context of the documentation.

The role of AI is not to replace the declarant or the customs expert. It helps prepare more reliable filings and allows human attention to be focused on complex or ambiguous situations.

Automate the verification of shipping invoices

A shipping invoice can be compared with shipping documents, the service provided, a quote, a negotiated rate schedule, or available operational data.

The goal is to identify discrepancies before payment or a dispute arises.

This approach goes beyond simply extracting the amount from an invoice to verify that it is consistent with the actual context of the transaction.

Perform cross-checks for AEO audits

Cross-checks are another example of the value of a case-based approach.

Instead of manually checking multiple documents and sources for each shipment, document-based AI can help automatically reconcile information and highlight discrepancies that require analysis.

This use case is a particularly good illustration of the shift from data extraction to multi-source verification.

What benefits does documentary AI offer logistics teams?

The value of documentary AI lies not only in the technical performance of the models. Its value is measured above all in day-to-day operations.

It helps reduce manual data entry, speed up the processing of complex cases, detect anomalies earlier, minimize certain operational errors, and enable teams to handle a higher volume of work.

At Docloop, several client case studies illustrate these benefits. A 600-page report was processed from start to finish in less than 15 minutes. In another case, the reconciliation of a 60-page packing list with a 60-page invoice—a process that previously took three days—was completed in five minutes. Another client reports saving about fifteen minutes per document.

These results illustrate the potential of document-based AI when applied to complex document-handling processes.

Can we trust a documentary AI?

The issue of trust is central to logistics, financial, customs, and regulatory processes.

Incorrect information can lead to billing errors, delays, inaccurate reporting, or inappropriate decisions.

The use of AI for document analysis must therefore be accompanied by oversight mechanisms.

The Risk of Hallucinations

Generative models can sometimes produce information that is plausible but incorrect.

This risk is particularly critical when the data involves financial amounts, quantities, weights, customs codes, or regulatory requirements.

A reliable information system must therefore do more than simply provide an answer. It must allow users to verify the information on which that answer is based.

Documentary Evidence

Critical information must be traceable to its source.

Ideally, this means being able to find:

• the original document;
• the version used;
• the exact location of the information;
• a comparison of sources;
• the validations performed.

This logic helps the user understand why an anomaly was detected or why a recommendation was made.

Auditability

In regulated environments, it is not enough to simply know that an audit has been conducted.

It must be clear what information was analyzed, what sources were compared, what anomaly was detected, what correction was made, and who approved the decision.

Auditability becomes even more important as systems shift from data extraction to recommendations and action.

Human-in-the-loop

The goal of documentary AI is not necessarily to eliminate all human involvement.

In critical processes, a clear division of roles is often more appropriate.

The system can:

detect → monitor → alert → recommend.

The expert may:

analyze → decide → approve.

This complementary approach makes it possible to automate repetitive tasks while maintaining human oversight of sensitive decisions.

Toward Document-Based AI Focused on the Case File and Business Context

Documentary AI has undergone several major developments.

OCR has made it possible to read characters. Machine learning has improved classification and extraction. IDP has structured and provided a deeper understanding of document information. Generative AI has made it possible to process a wider variety of documents. AI agents are now paving the way for systems capable of performing multiple steps in a process sequentially.

But in logistics and international trade, the real challenge lies not only in the power of the model.

It lies in the context.

To understand a transaction, an AI system must be able to determine which documents belong to the same file, how their information relates to one another, which business rules apply, which anomalies have been detected, and which validations have already been performed.

The next step in documentary AI is therefore not simply to read each document more effectively.

It involves understanding the relationships between documents, tracking the progress of a case over time, and supporting teams with verifiable, contextualized, and traceable information.

This trend is driven by several complementary mechanisms.

Document reconciliation makes it possible to automatically compare and verify information from multiple sources.

The Living Dossier maintains an evolving representation of the project and preserves its documentary context.

Trade Document Intelligence applies this document intelligence to the specific aspects of international trade.

The Document-Centric Agentic AI enables specialized agents to reason based on a persistent document repository, within a framework defined by business rules, oversight mechanisms, and auditability requirements.

The transition from a single document to a complete file thus represents a fundamental change.

Because, in international operations, the real challenge is no longer simply to answer the question:

"What is in this document?"

But to be able to determine:

"Is this file complete, consistent, reliable, and usable?"

This is the question that documentary AI must now learn to answer.

Frequently Asked Questions About Document-Based AI

What is the difference between OCR, IDP, and document-based AI?

OCR converts text in an image or scanned document into digital text. IDP goes a step further by classifying documents and extracting structured data. Document-based AI can encompass these capabilities and add contextual understanding, reconciliation across multiple sources, anomaly detection, and support for business processes.

How can AI process logistics documents?

AI can classify documents, extract relevant information, structure the data, and transmit it to systems such as TMS, WMS, or ERP. More advanced systems can also compare multiple documents from the same transaction to detect inconsistencies or missing items.

Can documentary AI detect inconsistencies between multiple documents?

Yes, provided that the system has reconciliation mechanisms and the necessary context to link the documents to the same transaction. It can then compare, for example, the weights, quantities, values, part numbers, or countries of origin listed in different documents.

What types of logistics documents can be processed by artificial intelligence?

Depending on the solution and use case, AI can process shipping orders, commercial invoices, packing lists, bills of lading, CMRs, AWBs, certificates of origin, customs documents, freight invoices, proofs of delivery, and other regulatory or operational documents.

Can documentary AI replace customs and logistics experts?

Documentary AI can automate repetitive tasks, detect anomalies, compare information, and generate recommendations. It does not necessarily replace human expertise, particularly when it comes to complex regulatory, customs, or operational decisions. Rather, its role is to help experts focus their attention on situations that truly require their judgment.

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