Documents and communications are the lifeblood of business. Everywhere a communication (such as an email or chat) or document is read or sent, they are the foundation of practically any procedure you can imagine. Therefore, it should come as no surprise that the intelligent document processing (IDP) industry is expanding at a rate of 28.9% annually and is projected to reach $17.8 billion by 2032.
IDP usually combines a variety of AI technologies, like as image recognition and natural language processing (NLP), to assist businesses in processing documents and communications quickly and efficiently. Document-based procedures have become the perfect application for agentic automation as corporate executives embrace AI agents—strong AI-based entities that can do tasks for and on behalf of people. Fast returns and significant efficiency gains can be achieved by combining AI agents with robotics and IDP capabilities under human supervision.
Opportunities For Intelligent Document Processing (IDP) Graphic
This begs the question, therefore, of whether you should purchase or develop your IDP capabilities. It’s critical to evaluate all available options and decide which strategy will provide the best performance, the quickest time to value, and the biggest return on investment. Purchasing an existing IDP solution is the best course of action for the majority of enterprises. Let me explain:
Create Your Own IDP (BYO)
Businesses now have more resources than ever before to assist them in creating unique IDP systems thanks to the proliferation of publicly available large language models (LLMs) and the expanding availability of supporting APIs. But having a lot of tools in your toolbox doesn’t make the work itself any simpler.
Every element of a BYO IDP system, from automation and data extraction to language comprehension, must be created from the ground up or, more frequently, supplied from several outside vendors. For instance, a company may base their IDP system on an external LLM like ChatGPT or Anthropic’s Claude in order to provide the NLP component required to comprehend documents and communications.
Compared to vendor systems, a BYO IDP system gives a company more customization options and end-to-end ownership. Without needing to collaborate directly with another firm, they are able to modify their system to meet evolving business requirements. However, these advantages are typically offset by the main drawbacks of the BYO strategy:
The Price
The idea that BYO is less expensive than purchasing IDP as a service is a prevalent one. This is typically not the case, either in the short or long term. Creating and managing your own IDP system requires a lot of effort and costly specialized skills. Software developers are needed to build the platform and user interface (UI), data scientists are needed to prepare and pre- and post-process data, and several other specialists are needed for activities like auditing, logging, and performance monitoring (for which you will need to design your own reporting dashboards).
Keep in mind that even third-party LLMs need engineers and AI professionals to customize the selected model to meet precise company needs. Even the most well-known and potent vision-language models (VLMs) are fundamental models that have been trained on a vast array of input types, including images and structured documents. They won’t be adjusted to the precise document type or schema you require by default, which results in less accuracy and more errors.
It also requires ongoing resources and modifications to maintain your own IDP system. User training materials are necessary for any BYO system that needs data annotation, and they must be updated to match your user interface.
Danger
It is dangerous to rely on costly and scarce technical expertise to maintain a system’s functionality. These teams are usually tiny due to budgetary constraints and a lack of skilled personnel. The amount of use cases and business units they can actually support may be limited. A system may become non-viable or non-performing over time due to talent loss. Additionally, there is always a chance that project funding will be withdrawn.
When AI expertise and model finetuning are needed, these difficulties are exacerbated. Nearly half (47%) of C-suite decision makers believe businesses are creating AI solutions too slowly, with talent skill shortfalls being the main reason, according to recent McKinsey & Company research.
Intricacy
The complicated AI model and platform governance are entirely your responsibility when developing an IDP system. In fact, systems designed for intricate use cases would need to maintain hundreds of AI models. To reach the required level of accuracy, for example, a big bank may require several hundred models that have been adjusted for different use scenarios. An AI system still requires a great deal of prompt engineering or context collecting to function well, even if it is able to process documents and communications “out-of-the-box.” It would be extremely challenging to scale this to hundreds of use cases because hundreds of prompts would need to be annotated, benchmarked, deployed, and maintained.
When you build your own IDP system, there are a lot of hidden costs. Every system component is a crucial choice, and every technology adds to technical debt (along with greater risk) and calls for specialized skills. BYO is unavoidably a burden and will take longer to value. The need for skill, governance, and maintenance is probably the cause of the higher lifetime cost. According to a Forrester analysis, it should come as no surprise that 69% of IT decision makers find document extraction and routing use cases to be extremely challenging to implement.
The Benefits Of Purchasing Your Own IDP System
Purchasing IDP as a service from a third-party vendor is an alternative to developing your own solution. There are two primary methods for doing this:
Connecting IDP with the remainder of your corporate technology stack after purchasing it as a point solution.
acquiring IDP as a component of a broader platform or solution. When necessary, this gives access to other features like automation.
Compared to a custom-built system, firms who purchase IDP as a service have less control over platform development. Platform suppliers will, however, collaborate with their clients to make sure the system adapts to their requirements.
It’s Time To Appreciate
It is typically quicker to implement an existing IDP platform than to create a new one. Well-established platforms have been used for many years in a variety of use cases in huge companies. Resources for training and enablement have already been developed, and professional services assistance is frequently offered to help users get started quickly and begin reaping the benefits of their initiatives.
Before they can be used in business, foundational LLMs need to undergo expensive and time-consuming fine-tuning and rapid engineering. Even then, some use cases could be so intricate or extensive that they become “dead ends,” where no amount of prompting can provide a precise, trustworthy extraction. In contrast, the AI that powers IDP as a service is usually built around low-code training experiences and quick model customization. For instance, they might use active learning, in which AI models and regular business users actively cooperate to speed up the training process.
Decreased Danger
Choosing IDP as a service significantly reduces risk in a number of ways. Your IDP system can now be maintained without the need for costly inside talent. Platform and model governance, as well as system maintenance, are owned by the vendor. Enterprise-grade data security at the highest levels is another expectation placed on IDP solution providers.
Ownership costs should also be taken into account. The adoption of soon-to-be-outdated technologies in a BYO IDP system, hurried development, or flawed code all increase the danger of technical debt. This eventually calls for expensive fixes and system enhancements.
Buying IDP as a service significantly lowers the buyer’s risk and technical debt. In order to remain competitive, vendors give priority to technical advancements, implementing the newest features and continuously refining and enhancing their offerings. They take care of the required testing, updating, reworking, and swapping out outdated parts.
Don’t try to construct the models on your own. Catchy headlines give the impression that it’s easier than it actually is. Look for a vendor who was using AI prior to the LLM craze. Proficiency in related AI procedures is required, including unsupervised learning, data preparation, and fundamental machine learning computations.
Scalability
To integrate with the pertinent corporate systems, a custom solution needs specially designed connectors and APIs. Hundreds or even thousands of development hours may be needed for this, depending on the business’s size and complexity. The most widely used enterprise systems will have pre-made connectors on well-known IDP platforms, allowing for quick integrations and time to value. You can anticipate having access to the newest AI advancements and capabilities with a cloud-based IDP system, all without having to pay for their development or integration.
IDP as a service ought to be the go-to choice for businesses looking for quick time to value, precision, and dependability in their IDP system because of all these factors. In the long run, custom builds increase risk and liability and attract significant technical debt. IDP as a service transfers accountability to a specialized platform that has been refined through years of competition and iteration.
An Adaptable, Open-Model Approach
The greatest AI model for your use case now might not be the best model in six months due to the rapid advancement of AI models. To provide the greatest customized LLMs for essential business operations, we are continuously investing in and refining them. That is demonstrated by the publication of our most recent model for analyzing complicated and unstructured documents.
Our AI strategy is still open, though. We offer the necessary tools to manage your preferred proprietary or third-party LLMs .
Optimization To Increase Precision And Dependability
RAG and tailored system prompting are two methods that maximize our IXP capabilities for sophisticated data extraction. Context grounding results in IXP models that are more accurate, safe, and efficient. The generated outputs may be readily validated with evidence thanks to our Validation Station interface, which displays proof of where the extracted information was located in the document.
Experience Working On Projects
Real-world IDP deployments in large corporations serve as the inspiration for our developments. Our users can utilize numerous models and only need to specify the type of document they want to use. They are also capable of managing model versions, monitoring, and performance evaluation—all essential skills for implementing, sustaining, and eventually growing AI throughout the company.