AI Platform for Invoice Processing and Specification Generation for a Logistics Company

About the client

The client works with a large network of overseas suppliers and subcontractors. Their document flow passes through several internal workflows and external systems, including Bitrix, 1C, and other services maintained by different vendors. In that environment, even basic invoice handling had become a multi-step operational process with heavy manual work and a large number of integrations.

Challenge

By the time we joined the project, the client already had an invoice recognition service in place, but it was not stable enough and struggled under high load. At the same time, the business needed much more than field extraction. It required a reliable workflow for multiple roles at once: translators, logistics staff, and managers. The same document entered the process through different channels, followed different scenarios, and had to be transformed into different output formats for different departments. Invoices arrived in multiple languages, suppliers described products in inconsistent ways, and some documents contained 100-150 line items.

The workflow also included logistics-specific checks, such as sanctions screening, validation of disputed product attributes, and generation of specifications for downstream operations. A standard OCR layer was not enough for that scenario. The client needed a custom solution that matched its internal business logic and could survive seasonal traffic peaks.

Solution

Unified document intake

We built a module that works with several intake points. Documents can enter the workflow through Bitrix, a dedicated frontend upload flow, or directly from 1C. This made invoice processing part of the client's real operational environment rather than a standalone recognition tool.

Recognition and data extraction

At the core of the system is a recognition module that handles the main extraction logic for supplier invoices. It processes multilingual documents, detects required fields, and prepares structured data for downstream steps. The design was tailored to the actual production environment: high document volumes, inconsistent supplier formats, and long line-item tables.

Specification generation based on business rules

After extraction, the system does more than store the recognized fields. It applies the client's business rules, translates entities, normalizes values, and generates final specifications in the required format. This was a critical part of the project because the specification itself became a working document in Russian and served as the basis for subsequent operations. That business-rule layer is where the custom solution delivered most of its value.

Dedicated workflow for logistics teams

We also configured a separate document-processing path for logistics teams, with their own set of extracted fields and output documents. In addition to core invoice handling, this workflow included extra checks relevant to logistics operations, including sanctions screening for goods. As a result, the same incoming document could support multiple operational workflows without repeated manual parsing.

Human in the loop and quality control

We did not try to eliminate human involvement entirely. The platform keeps a human-in-the-loop workflow, allowing staff to review outputs and correct exceptions where needed. This was the most practical approach for a high-volume environment with multiple suppliers, multiple languages, and a high cost of mistakes. In parallel, we helped establish an analytics layer so the client could monitor extraction quality and see which fields required the most manual correction.

Reliability under peak load

A major part of the project was not only recognition quality, but operational stability. During seasonal peaks, the previous approach could not handle the load reliably. We redesigned the architecture, adapted prompts and validations for a more stable production setup, and eventually moved to a lighter-weight model that handled large request volumes better. This removed the main availability bottleneck the client had experienced during peak periods.

Extending the platform with advanced validation

As the platform evolved, we started adding more specialized scenarios. One of them was external validation of disputed product attributes, helping the client verify color and size against trusted online sources before the next processing stage. In this domain, even small mismatches in product color naming can create issues later in the workflow. The architecture was designed from the start to make that type of custom validation part of the overall process.

Result

The client received more than an improved OCR component. They got a working AI platform for invoice processing in a real multi-system environment. The solution automated key workflows for translators and logistics teams, reduced manual effort, improved recognition quality, and made the service significantly more stable under heavy load. At the same time, the platform remained flexible enough for further extensions, including more advanced validation scenarios and future email intake automation with draft response generation.