Benchmark of the best IDP solutions in 2025: towards intelligent document processing

In 2025, document efficiency is a logistical emergency

"A supply chain is only as fluid as the data that flows through it. This sentence perfectly illustrates the importance of intelligent document processing (IDP) in the logistics sector in 2025. The exponential increase in document volumes linked to the transport of goods, combined with increasing regulatory complexity, is forcing a profound transformation of processes. IDP is no longer just a technology, but a strategic lever for competitiveness and compliance for logistics operators, customs declarants and IT decision-makers.

According to Straits Research, the global IDP market was valued at 2.44 billion USD in 2024 and will reach 3.3 billion by the end of 2025, with an annual growth rate of 35.4% until 2033 1. This dynamic can be explained by increased demand in sectors such as finance and healthcare, but above all in transport and logistics, where digitalization requires document control. Companies are faced with ever larger and more varied document flows, and processing them manually generates delays, costs and errors. IDP provides an answer to these challenges by automating data extraction and streamlining the flow of information.

In this article, we review the top-performing IDP solutions of 2025, their features and differentiators. We devote a special section to transport and logistics, comparing Docloop - a specialized European solution - with its competitors (Extend, Nabu, Docsumo, Affinda, ABBYY, etc.), in an objective and factual manner.

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What will the IDP be in 2025?

The intelligent document processing (Intelligent Document Processing, IDP) combines advanced OCR, Artificial Intelligence and automation to extract, structure, validate and integrate data from complex documents. In 2025, IDP solutions go far beyond simple text recognition: they understand the context of information and integrate with business processes.

In concrete terms, a modern IDP platform can :

- Read all types of documents, even unstructured ones (emails, PDFs, images, handwritten forms) and automatically extract relevant data. For example, it recognizes an invoice, contract, or delivery note and knows how to isolate key fields (header and footer information, dates, amounts, references, line-by-line details, etc.).

- Classify and verify information: AI can categorize documents (invoices, packing lists, shipping orders, certificates, etc.) and check the consistency of extracted data (verifying amounts, numbers, etc.), drastically reducing human error.

- Feeding downstream systems: the IDP interfaces with the company's ERP, WMS (Warehouse Management System), TMS (Transport Management System), or EDM (Electronic Document Management) systems to inject data, minimizing manual re-entry and speeding up workflows.

- Continuous learning: thanks to machine learning, these solutions improve their accuracy over time. The best ones now achieve over 98% accuracy, reducing processing costs by 40 to 80% depending on the sector and level of automation.

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In the logistics sector, IDP is particularly used to automate the processing of delivery notes, transport invoices, customs documents, CMRs, packing lists, sea and air manifests, avoid re-entry into business systems (TMS, customs, etc.) and ensure regulatory compliance (e.g. error-free customs declarations). By 2025, these solutions will be essential to absorb the growth in document exchanges, while maintaining efficiency and compliance.

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Overview of IDP solutions on the market in 2025

The IDP market in 2025 will be served by a variety of players, from long-established publishers to innovative start-ups:

- Longstanding leaders across multiple sectors: Companies such as ABBYY, IBM, and Kofax have long offered robust OCR and automation suites that are popular with large enterprises. For example, ABBYY Vantage is a recognized cloud platform that quickly extracts data from many types of documents (invoices, receipts, contracts, etc.) and integrates with existing systems.

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These solutions are characterized by high precision and extensive integration functionalities, at the price of a certain complexity in implementation, and less flexibility in varying document formats. According to Gartner and Forrester, ABBYY will remain one of the leaders in the IDP sector in 2025.

- New cloud-native platforms: New entrants such as Extend and Rossum are leveraging AI and the cloud to deliver greater flexibility. Extend, for example, offers a platform built entirely on large language models (LLMs) and modern tools, enabling technical teams to create complex document processing pipelines in days rather than months. They often target IT teams and data scientists, providing a modern API and cutting-edge development tools, while promising high accuracy (>95%) even on degraded scans. For its part, Rossum (originally from Europe) relies on ready-to-use AI that can automatically adapt to new formats, widely used for entering invoices and financial documents. In general, these "AI-first" cloud solutions offer agility and are often prized by SMEs for their rapid deployment. The use of a single model, the difficult management of large documents (several dozen pages), the difficulty of retraining, and the inability to view the extraction results on the original document make these solutions effective for certain use cases and less relevant for others. 

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- Specialized solutions by use or industry: some platforms focus on a specific area. For example, Nabu is an intelligent agent dedicated to customs clearance —this system autonomously manages the preparation of customs declarations from start to finish, integrating directly into existing customs software. Similarly, there are offerings focused on a specific type of document: Affinda made a name for itself with its CV parser and now offers a Document AI API capable of processing CVs, invoices, passports, and bills of lading (BL) to extract structured JSON. Docsumo, meanwhile, targets the needs of operational teams in finance and insurance: its ready-to-use platform automatically extracts datafrom invoices, bank statements, insurance forms, etc., with pre-trained models for these use cases and the ability to customize via machine learning. These specialists often offer predefined models that perform well in their niche, at the cost of narrower functional coverage, and therefore the need to maintain multiple solutions to handle all user needs. 

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- No-code and UX-oriented players: We are also seeing the emergence of solutions that prioritize a simplified user experience for business users. For example, Docloop (see dedicated section) and Docsumo feature user-friendly interfaces that allow operational teams to manage part of the processing themselves, whereas traditional tools require the intervention of developers. Others, such as Hyperscience, offer a "human-in-the-loop" approach where the end user can train the AI by validating/correcting extractions, which is well suited to regulated environments (banking, insurance).

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In short, the IDP 2025 offering is rich: powerful but complex generalist platforms, agile cloud solutions focused on developers, and specialized or business-oriented tools. What are the key differences? That's what we analyze below.

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How will IDP solutions differ in 2025?

There are several criteria for comparing IDP solutions on the market in 2025:

- Coverage of use cases: some solutions are generic multi-use cases (able to process any type of document, from invoices to contracts and emails), while others are single-use cases and optimized for a specific use (e.g., Nabu for customs declarations, or tools that only manage supplier invoices). A solution covering all types of documents offers aone-stop-shop approach, whereas a hyper-specialized tool may excel at its single task but will need to be supplemented by others for adjacent needs.

- Ready-to-use vs. customization: platforms differ in the degree of configuration required. The best IDPs in 2025 offer pre-trained models for common documents (invoices, passports, resumes, delivery notes, etc.), which can be used immediately with minimal effort. This is the case with Docloop and Docsumo, which provide ready-to-use models for many transport, logistics, finance, and other documents. Conversely, solutions such as Extend and Affinda take a "toolbox" approach: they offer the API and AI engine, but it is up to the company to build and train its own models for each specific document format (no models are provided as standard). This offers total flexibility, but at the cost of significant technical work to achieve a high level of accuracy.

- User experience and target audience: solutions geared toward IT/developers can be contrasted with those geared toward business/operations. For example, ABBYY and Extend are mainly aimed at technical teams (data/IT)—their implementation requires advanced skills and their user interface is complex, designed for specialist integrators or administrators. ABBYY FlexiCapture is extremely powerful but not very accessible to non-technicians, which often limits its use to large projects led by IT departments or external service providers. Docloop and Nabu, on the other hand, have focused on simplified UX: Docloop offers an ergonomic web interface where a transit agent or customs declarant can supervise extractions without coding, while Nabu works in the background, integrating with existing tools so as not to disrupt users' habits. Similarly, Docsumo targets finance professionals with a ready-to-use interface, although some advanced users still find it a little complex. UX is a major challenge: a plug-and-play, user-friendly solution will enable faster adoption by business teams, whereas a tool that is too technical is likely to remain underused.

- Support and customization: Implementing IDP affects critical business processes, hence the importance of support. Publishers have different approaches to this issue. Some offer tailored support ("By your side") to help configure and train customer-specific models. This is the case with Docloop, whose technical team works with customers to refine the AI on their own documents, and Docsumo, which offers customization services. Others sell the tool on a self-service basis ("On your own"): it is up to the company to configure the IDP according to its needs, possibly via its integrator. Extend, for example, provides comprehensive documentation and APIs, but little direct human assistance in creating your custom pipelines. ABBYY offers training and certification, but in practice, companies often need to hire an expert integrator to adapt the solution.

- Interoperability and APIs: Finally, the ability to integrate with other systems is a key differentiator between solutions. Most offer APIs, but not all are designed to be embedded as OEM (Original Equipment Manufacturer) solutions by third-party publishers or easily integrated into business software. Docloop has made itsopen API a key selling point, targeting transport/customs software publishers who want to include IDP in their offerings. Extend and Docsumo also provide robust REST APIs that allow you to connect their services to your workflows. On the other hand, highly vertical solutions such as Nabu are more monolithic: they integrate with the customer's TMS/customs software via connectors, but are not designed to be resold or customized by a third party (no open developer program). Affinda falls somewhere in between: it does offer a Document AI API for technical integration, but the company remains the only provider without a true reseller program (the offering is aimed more at end customers such as HR or accounting services than at partner software publishers).

The comparative table below summarizes some of these differences for six representative IIP solutions:

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2025 comparison - IDP solutions (Docloop vs. competitors)

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The rise of hybrid platforms combining AI and automation

Some platforms are moving toward integrated approaches that combine traditional document extraction with workflow automation. Raft, for instance, targets tier 1 global logistics organizations with a solution that blends data extraction and business logic on top of TMS/WMS/ERPs that don’t always integrate such rules. However, despite appearances, these platforms typically require significant development effort, with workflows tailored one use case at a time. Implementation can be time-consuming and often involves rethinking and adapting existing processes. Compared to more flexible, general-purpose IDP solutions, these tools may offer less adaptability and come with higher integration overhead, with no proven better results & ROI than the IDP solutions described in this article.

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What you need to remember when making your technology choices

By 2025, Intelligent Document Processing will be a cornerstone of digital document transformation. Over and above the immediate benefits (reduced data entry costs, faster processes, drastic reduction in data entry errors), IDP paves the way for new inter-company operating modes (data shared in real time, paperless supply chains, etc.).

For IT and business decision-makers, choosing the right IDP solution is not just a matter of comparing OCR rates: you also need to assess the tool's business understanding, its interoperability with your ecosystem, and how easily it can evolve to handle more complex use cases (e.g., integrating generative AI to automatically fill in missing fields, produce document summaries, etc.). Feedback shows that successful projects are those that combine powerful technology with solid support to adapt it to specific business processes. Docloop is part of this new wave, positioning itself at the crossroads of AI, document logistics, and transportation & logistics compliance. Its multi-AI approach (intelligence tailored to each type of document rather than a single generic model), unified user experience, AI Studio that simplifies the work of customers' IT teams, API that connects to any industry tool, and vertical specialist DNA illustrate the direction IDP is taking: ever smarter, ready-to-use solutions that are finely tuned to business needs. It is this alignment between technology and the field of application that will guarantee the success of document automation projects in the coming decade.