AI DOCUMENT READING & CUSTOMS DATA EXTRACTION
AI reads invoices, packing lists, and automatically creates declarations, reducing errors and shortening customs document processing time. Learn how EasyCus operates.
In import and export operations, documents are a critical data source for the customs declaration process. From commercial invoices and packing lists to bills of lading and related documents, import-export personnel must collect, check, cross-reference, and input vast amounts of information before finalizing declaration data.
As the volume of shipments increases, the workload of processing documents grows accordingly. Manual data entry is not only time-consuming but also creates numerous repetitive tasks, especially when information is presented in various formats.
In this context, AI customs document reading can help enterprises automate the data extraction process from documents and reduce reliance on manual data entry. Combined with data validation functions and other input methods, this technology is being integrated into customs declaration software to help enterprises enhance document processing efficiency and prepare declaration data.
EasyCus is the solutions created by Paracel for web-based customs declarations, developed with the orientation of applying AI and automation to the document processing workflow while supporting multiple data input methods tailored to the practical needs of import-export enterprise.
1. How Much Time Are Import-Export Personnel Losing to Manual Document Data Entry?
A set of documents for customs declaration can contain various groups of information, such as: HS codes, item names, quantities, unit prices, values, delivery terms, buyer information, seller information, container numbers, bill of lading numbers, and other shipment-related data.
However, this information is not always presented in a uniform structure. Invoices may be provided in PDF format, packing lists may use an entirely different template, while bills of lading have their own layout. For documents scanned or photographed from paper copies, image quality and formatting may also vary.
In traditional workflows, import-export staff typically must perform multiple sequential actions: read the document, identify the information to use, enter data into the system, and cross-check with the original document. When processing a small batch of documents, the time spent on these actions may not have a significant impact. However, for enterprise with large shipment volumes, repeating this process numerous times becomes a substantial part of the daily workload.
Besides processing time, manual data entry also introduces the risk of errors during the transfer of information from documents to the system. Mistakes such as mistyping quantities, unit prices, product details, or omitting data can lead to the need for review and adjustments.
Therefore, the challenge is not only shortening data entry time, but also reducing manual steps while maintaining the ability to check and control data before declaration.
This serves as the basis for applying AI customs document reading to the declaration workflow, aiming to help reduce repetitive data entry steps and improve input data processing efficiency.
2. What Is AI Customs Document Reading and Data Extraction?
AI document reading is a technology that supports recognizing and extracting information from documents such as PDFs, images, or scans, then converting this information into structured data for further processing on the system.
One of the foundational technologies commonly used in this process is OCR (Optical Character Recognition) - optical character recognition. OCR helps the system recognize characters appearing in images or documents and convert them into text data.
However, pure OCR mainly focuses on recognizing content, whereas an AI document reading system needs to further solve the problem of determining the meaning and position of data within the document structure. For example, when the system recognizes a value like "500 PCS", the issue is not just identifying what this character string is, but also determining that this is quantity information and needs to be routed to the corresponding data field.
The two technologies can be simply distinguished as:
- OCR: character recognition and converting content from images into text.
- AI data extraction: analyzes document content and structure to identify necessary information fields, thereby structuring the data appropriately for further processing.
Compared to manual data entry, AI can significantly help reduce repetitive information entry tasks. However, data extracted by AI still needs to be checked and verified before being used for the next steps.
3. AI Document Reading and Declaration Creation Process on EasyCus
With EasyCus, AI is integrated into the document data processing workflow, helping users reduce manual data entry tasks and structure information for declaration data.
The process can be executed through four main steps:
1. Uploading the document set
Users upload documents that need processing to the system, such as invoices, packing lists, or related documents. Uploading documents directly to the platform helps reduce manual data conversion steps before starting the data entry process.
2. AI analyzes and extracts data
Upon receiving the documents, AI analyzes the content and extracts the necessary information. Instead of staff having to read each document and re-enter each data field, the system supports transferring information from documents into structured data.
This is a step focused on reducing the volume of repetitive data entry work during the preparation of declarations.
3. Cross-checking data with original documents
The extracted data needs to be reviewed before further processing. EasyCus supports users in reviewing the data and cross-checking it with the original documents, thereby verifying the information recognized by AI and making adjustments when necessary. This approach maintains the control role of import-export personnel, especially for information with high accuracy requirements.
4. Creating data for declaration purposes
After reviewing and finalizing the data, users can proceed with further processing steps to create data for declarations on the system. A notable point in this process is that data verification is performed before moving to the declaration step, rather than only discovering issues after completing the data entry process.
As a result, AI becomes a supporting layer in the declaration workflow, combined with the user's review and verification capabilities.
4. Why Paracel Developed Easycus with an AI + Automation Approach
EasyCus is developed by Paracel Technology Solutions, with the strategic direction of applying AI and automation to practical enterprise operational challenges. In import-export operations, document data entry is just one step in the entire process. If data entry is automated without connecting to subsequent verification, standardization, and processing steps, the efficiency of automation remains limited.
Therefore, EasyCus is developed to combine AI, automation, and validation, focusing on the processing chain from input data to data ready for declaration.
The overall workflow can be visualized as follows:
Original documents → AI data extraction → checking and reconciliation → validation → data ready for declaration.
This approach helps solve problems at the process level rather than merely optimizing a single isolated task.
At the same time, Paracel's experience in developing technology solutions for enterprises serves as the foundation for building EasyCus in a way that aligns AI with practical operational needs. The goal of EasyCus is not only to recognize information from documents, but to help enterprises shorten the process of converting data from documents into the declaration system while maintaining necessary professional verification steps.
5. Benefits of Using AI for Document Data Extraction Compared to Manual Entry
For enterprises using customs declaration software, integrating AI into the data extraction process can generate significant improvements in document preparation and data entry.
5.1 Reduction in Manual Data Entry Tasks
AI assists in extracting information from documents, thereby reducing the amount of data that staff must read and re-enter manually. This is especially meaningful when enterprises process multiple sets of documents or numerous line items in a single shipment.
5.2 Reduced Risk of Errors During Data Entry
Manual data re-entry from documents into the system always carries the risk of discrepancies due to human error. Automating the extraction step reduces the number of re-entry operations, thereby limiting a common source of errors during the data preparation process.
However, AI does not completely eliminate the need for verification. Users still need to cross-check the extracted data against the original documents before confirmation.
5.3 Data Standardization
Documents from various partners may differ in format, layout, and information placement. Bringing data into a unified structure within the system makes the processing workflow more consistent while reducing dependence on the layout of each document type.
5.4 Increased Document Processing Speed
When the data extraction portion is supported automatically, staff can reduce the time spent on repetitive data entry tasks. Instead of focusing most of their time on entering data, employees can dedicate more time to verifying information and handling cases requiring professional expertise.
5.5 Support for data control before declaration
Combining AI with checking and validation steps creates an additional layer of control before data is used for subsequent declaration steps. This is a critical factor for import-export enterprise, where data must be thoroughly checked before official declaration.
6. What Declaration Creation Methods Does EasyCus Offer Besides AI?
AI document reading is one of the data entry methods on EasyCus, but not the only one. In practice, enterprise may have various data sources. Sometimes data resides directly on documents, but in other cases, enterprise already have data available as files or need to enter it directly into the system.
Therefore, EasyCus supports three main methods:
6.1 Excel/TXT/XML Import
Enterprise can import data from Excel, TXT, or XML formats when data is already available. This method helps leverage existing data and avoids re-entering information that has been prepared in advance.
6.2 Manual entry with validation
In cases where data needs to be entered directly, users can perform manual data entry on EasyCus. Data is checked against validation rules integrated into the system, helping users control information during the entry process.
6.3 AI document reading and extraction
When input data resides in documents such as invoices, packing lists, or related documents, users can leverage AI to assist with information extraction.
These three methods help EasyCus address various data processing scenarios:
- AI is suitable for data that needs to be extracted from documents.
- Excel/TXT/XML Import is suitable for data already available in file formats.
- Manual entry is suitable for cases requiring direct data entry or adjustment on the system.
As a result, enterprise are not limited to a single data entry method but can choose the approach that best fits their data sources and current operational workflows.
With EasyCus, AI is not implemented as an isolated feature but is integrated into the overall data processing workflow, combining file imports, manual entry, and validation. This provides the foundation for enterprise to gradually transition from manual data entry processes to a declaration workflow with higher levels of automation and data control.
7. Frequently Asked Questions (FAQ)
Who developed EasyCus?
EasyCus is developed directly by Paracel Technology Solutions, specializing in ERP (Odoo) implementation solutions and building AI/automation products tailored to the actual operational needs of the enterprise.
How does EasyCus AI read and extract document data?
The system receives document files in PDF or scanned image formats (invoices, packing lists, bills of lading, etc.), then AI analyzes and populates the data into corresponding fields on the customs declaration. Thanks to the control step right from the original documents, errors are detected before the declaration is created, rather than having to be fixed afterwards.
What happens if the AI reads or extracts incorrectly?
EasyCus features a data preview step before generating the declaration, allowing users to cross-check directly with the original documents and make adjustments if necessary before confirmation. This is the key difference compared to systems that only check for discrepancies after declaration submission.
Besides AI, are there other ways to create declarations in EasyCus?
Yes. Enterprises can import data from Excel/TXT/XML, or enter it manually with strict business validation.
Does EasyCus require software installation?
No. EasyCus is a web-based platform that works across multiple operating systems without complex installation.
Does EasyCus offer a free trial?
Contact the EasyCus team directly for consulting on a trial package suitable for your enterprise's scale and declaration needs.
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Sales representatives can use these scores to prioritize high-potential opportunities rather than relying solely on subjective assessment. Key Features Manage leads, contacts, accounts, and sales opportunities. Track pipelines and revenue forecasting. Automate certain sales activities. Support reporting, data analytics, and AI applications based on the subscription tier. Expand through tools and applications within the Salesforce ecosystem. Suitable Enterprise: Medium and large enterprise, especially organizations with complex sales processes requiring an advanced CRM ecosystem and scalability. 2.3. HubSpot CRM HubSpot CRM focuses on connecting customer data with Marketing, sales, and customer service activities. The platform serves over 306,000 customers in more than 135 countries. HubSpot also offers a free CRM version with no time limit, supporting contact management, deals, and interaction history. Key Features Manage customer profiles and interaction history. Track leads and sales pipelines. Support for email, forms, scheduling, and various marketing tools. Provide reports, nurturing tools, and automation depending on the plan. Connect data across Marketing, Sales, and customer service departments. Advantages HubSpot features a relatively accessible interface, suitable for enterprises looking to connect marketing and sales activities. Free tools also help small teams get familiar with CRM before investing in advanced features. Limitations The scope of free usage has limitations on users and features. When the enterprise needs deep automation, advanced reporting, or multiple functions within the same ecosystem, costs can increase significantly. Suitable enterprises: Startups, small and medium enterprises, and Marketing-Sales teams that need close collaboration in attracting, nurturing, and converting customers. 2.4. Zoho CRM Zoho CRM integrates the Zia AI assistant to support analysis and forecasting in the sales process. Specifically, Zia can score leads, predict the probability of winning or losing a deal, and estimate revenue based on available data. As a result, the sales team has additional basis to prioritize opportunities and identify deals that require closer follow-up. Key Features Manage leads, contacts, accounts, and deals. Set up workflows and follow-up reminders. Track sales performance through reports and forecasts. Support analytics, lead scoring, and AI features depending on the version. Connect with other applications within the Zoho ecosystem. Advantages Zoho CRM offers multiple versions suitable for enterprises of various scales. Its diverse ecosystem helps enterprises consider expanding from CRM to tools supporting marketing, customer support, and task management. Limitations Features and usage limits vary across plans. If an enterprise uses multiple applications or needs integration with systems outside of Zoho, connectivity capabilities and total operating costs must be carefully evaluated. Suitable enterprises: Small and medium enterprises looking to manage sales, automate processes, and gradually expand their system using related applications. 2.5. Microsoft Dynamics 365 Sales Microsoft Dynamics 365 Sales is suitable for enterprises looking to connect customer management with the Microsoft working environment. Through Copilot, the system can summarize lead profiles, accounts, and sales opportunities; aggregate recent changes; and assist with drafting emails and preparing for meetings. This information helps staff quickly grasp the history and status of transactions before interacting with customers. Key Features Management of leads, contacts, and sales opportunities. Tracking of deal pipelines and revenue forecasting. Integration with compatible Microsoft tools, such as Outlook and Microsoft 365. Support for data analytics and AI features depending on the version and license. Process customization according to sales management needs. Advantages Dynamics 365 Sales is worth considering for enterprises already using the Microsoft ecosystem and looking to enhance collaboration between sales data, productivity tools, and analytics. Limitations Enterprises need to carefully evaluate licensing requirements, feature scope, and implementation costs. Deep customization or connecting multiple systems may require specialized resources. Suitable enterprises: Medium and large enterprises, especially those already utilizing Microsoft 365 or needing an analytical and scalable sales management platform. 2.6. Pipedrive Pipedrive focuses on helping sales teams track every deal through the stages of the sales process. Users can customize pipelines, monitor required activities, and identify stalled opportunities. The Insights reporting tool supports analyzing metrics such as deal volume, conversion rates, and pipeline bottlenecks. Key Features Management of leads, contacts, and deals. Setup of sales stages aligned with enterprise processes. Reminders and tracking for customer care activities. Support for email synchronization, automation, and reporting depending on the plan. Connection with numerous external applications via integrations. Advantages Pipedrive features a sales-focused approach, making it easy for sales teams to visualize active opportunities and avoid missing follow-up activities. The pipeline interface also helps managers quickly grasp deal progress. Limitations Pipedrive leans towards sales management; therefore, enterprises with comprehensive operational management needs such as inventory, accounting, and manufacturing may require integrating additional systems. Advanced automation and management features depend on the subscription plan. Suitable Enterprise: Enterprises with sales teams that need clear pipeline management, opportunity tracking, and improved closing performance without immediately implementing an overly broad management system. 2.7. Bitrix24 Bitrix24 combines customer management with collaboration tools and a contact center. Enterprises can connect channels such as website forms, live chat, email, telephony, and supported messaging platforms. Incoming messages, calls, or form submissions can be logged as leads, deals, or updates to existing profiles. Key Features Customer, contact, and deal management. Reception of requests from sources integrated into the CRM. Automation of certain sales and customer care activities. Manage tasks, projects, schedules, and internal communication. Provide reports and advanced features based on the plan. Advantages Combining CRM and collaboration tools helps employees manage customers, assign tasks, and coordinate work within the same environment. Bitrix24 also offers a free plan with a certain scope of use for enterprises to experience before upgrading. Limitations Due to the integration of multiple feature sets, the interface and setup process may take some time to get used to. Enterprises should check the limits of each plan, especially when managing multiple branches, complex access permissions, or deep automation. Suitable enterprise types: Enterprises looking to combine customer management with internal collaboration, task assignment, and progress tracking on a single platform. >> See More: WHAT IS CUSTOMER SERVICE? WHY IS IT NECESSARY TO UNIFY CUSTOMER DATA? 3. Which CRM Software Should Enterprises Choose? 3.1. Small enterprises needing easy-to-use, low-cost CRM For small enterprises, the initial priority should be centralized customer information management, interaction history tracking, and sales opportunity control without adding operational burdens. However, enterprises need to consider user limits, features, scalability, and costs when upgrading. If there is an early expectation to manage orders, inventory, or accounting on the same system, solutions with scalability from the outset should be considered to minimize future data migration. 3.2. B2B enterprises with multi-stage sales processes B2B enterprises typically need to track customers through multiple steps, from prospecting, consulting, and quoting to negotiation and contract signing. If information is scattered across emails, spreadsheets, and personal notes, managers will find it difficult to identify which opportunities are stalling or need support. Pipedrive suits visual pipeline tracking needs; Salesforce and Microsoft Dynamics 365 Sales are worth considering when enterprises need to manage complex processes, deep analytics, or control operations across multiple sales teams. When connecting sales opportunities with quotes, orders, and operational workflows is required, enterprises should consider Odoo CRM. 3.3. Enterprises with multiple customer care channels When customers reach out via website, email, phone, Zalo, or Messenger, the challenge is not only receiving messages but also maintaining a seamless support history. Staff need to know whether the customer has contacted before, who is in charge, and the current resolution status. Enterprises should prioritize CRM systems capable of integrating currently used channels, recording interaction histories, and routing requests to the appropriate staff. Before making a selection, verify the connectivity capabilities for each specific channel, especially messaging platforms popular in Vietnam. Not all channels are supported out-of-the-box in every service plan. 3.4. Enterprises with multiple departments and branches that need data synchronization For multi-branch enterprises, customer data can be duplicated or scattered across units. A customer who has purchased at one branch and contacts another may still be treated as a new customer if the information has not been synchronized. In this case, enterprises should prioritize systems with a centralized customer profile, role-based access control, customer assignment mechanisms, and branch-level reporting. At the same time, it is necessary to clearly define which data is shared system-wide and which data is restricted to specific departments or branches. 3.5. Enterprises needing a CRM connected with ERP and management operations If a CRM only needs to manage contacts and sales opportunities, a standalone solution can meet these needs well without deploying other management modules. However, when staff need to check inventory, order status, debt, or purchase history to consult customers, using separate systems can increase cross-checking steps and the risk of data discrepancies. At this point, enterprises should consider a CRM embedded within an integrated ERP system like Odoo. When properly configured, customer data can connect with sales, inventory, accounting, and related operations, helping departments collaborate on a single platform. The point to consider is not only the number of features, but also the true scope of integration needed. If the enterprise already has an ERP or dedicated accounting software, the cost and complexity of connection must be evaluated before migrating the entire system. 4. Criteria to Consider Before CRM Implementation 4.1. Define objectives and customer management processes Before choosing software, enterprises need to identify priority issues: lead leakage, lack of care history, difficulty controlling sales performance, or data scattered across branches. From there, build customer management processes, sales stages, and responsibilities for each department. The clearer the objectives, the easier it is for enterprises to determine necessary features and avoid investing in functions that are not yet needed. 4.2. Evaluate integration capabilities with existing systems The CRM must be able to connect with tools serving business operations such as websites, email, call centers, marketing platforms, sales software, or accounting. Before implementation, it is necessary to clarify how data will be synchronized in terms of direction, update frequency, and which system is the primary data source. For multi-branch enterprises, it is advisable to further test the ability to limit duplicate data, manage shared information, and aggregate reports across the entire system. 4.3. Balance licensing, implementation, and operational costs CRM costs consist of more than just subscriptions or software licenses. Enterprises must factor in the costs of process setup, data entry and cleaning, system integration, user training, maintenance, and upgrades. A solution with a low starting price may not necessarily have a low total cost if it requires purchasing many additional utilities or custom development. Therefore, total projected costs over several years should be compared against the operational efficiency the system can deliver. 4.4. Ensure data security and access control A CRM stores contact information, transaction history, and customer interaction data, so it requires an appropriate protection mechanism. Enterprises should evaluate role-based access control, action history logging capabilities, data backup and recovery mechanisms, as well as account management procedures when personnel change. With a multi-branch model, it is necessary to clearly define the view, edit, and export data permissions for each user group to both support collaboration and restrict access outside of job scopes. 4.5. Choosing the implementation and post-sales support partner The right software may still underperform if the implementation process lacks clarity. Enterprises should consider the partner's industry implementation experience, business survey capabilities, data migration, system integration, and user training. Additionally, it is essential to agree on the scope of work, implementation timeline, acceptance criteria, support policies, and potential additional costs. A partner who understands practical workflows will help enterprises choose the right configuration rather than merely installing software based on out-of-the-box features. 5. Conclusion CRM software helps enterprises manage customer information, track sales activities, and enhance cross-departmental collaboration. However, choosing the right solution should be based on scale, business processes, integration needs, and budget, rather than simply comparing the number of features. Paracel Technology Solutions provides consulting and Odoo implementation based on the enterprise's actual workflows, from system configuration to customization and integration when needed. The goal is to build a consistent data flow, support centralized customer management, and improve collaboration between departments and branches. Want to learn how to unify omni-channel, multi-branch customer data with Odoo? Register for the webinar "Customer Care Digital Transformation: Unifying Omni-Channel, Multi-Branch Data With Odoo" co-organized by Odoo and Paracel Technology Solutions at 14:00-15:00 on November 11, 2026 to discover the solutions and how to apply them to your enterprise's reality. 👉 Registration link: https://erp.pworkspace.com/r/quE
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