Inside the Buyer’s Guide to CDPs: Build vs. Buy

AI tools are not like other software. Not only do you need machine learning, AI, and software talent, you need data – carefully organized, industry specific data that the average company, or even enterprise, would find hard to provide. This data is part of the reason why developing an AI model is more resource-intensive than you would expect. If you’re asked “why can’t we build this ourselves?” The answer is probably a combination of data, expertise, and cost. 

It may not be, however, and in certain cases it can be desirable to build an AI model yourself. Whether you build or buy will depend on your resources, budget, and financial capabilities – which is why we created this series—to help guide your organization through this complex decision and evaluate the options available.

Wisedocs recently released 2025 Buyer’s Guide aims to help claims leaders evaluate AI-powered Claims Documentation Platforms (CDPs) as they embark on this journey. Our Buyer’s Guide blog series will cover everything you need to know about selecting a CDP—whether that means building, buying, or finding the right path in between.

What to Consider Before You Build a Claims Document Platform

According to the Boston Consulting Group, 75% of executives consider AI one of their top strategic priorities in 2025. Many of these companies are facing the same question: build versus buy. 

When the financial data and media company Bloomberg embarked on training its own AI, it required over 700 billion tokens, or about 350 billion words. It also required 50 billion parameters and 1.3 million hours of processing unit time. Creating your own domain-specific model is not a common approach, since it requires so much high quality data to train. Most companies don’t have access to these resources, nor do they have the computing power or data science knowledge behind it. 

In some cases, though, they do. Bloomberg invested in its BloombergGPT model after its researchers and researchers from John Hopkins suggested that smaller models are the way to go, especially when it comes to domain specific applications like finance. 

But the key question to ask is which one works for you? When considering whether to build or buy your CDP, think about whether your organization can generate:

• Billions of data points with metadata, organized for your use case
• A full team of product managers, data scientists, software engineers, and subject matter experts, often with medical or industry experience
• Ongoing support for the model, training it, and making adjustments according to market conditions and customer expectations
• Data security that supports end to end privacy in compliance with privacy laws
• Round the clock support for AI hallucinations or errors
• Months (or even years) to train, design, launch and maintain this model

When to Buy a Claims Documentation Platform

Building your own CDP can be the best option in some scenarios, but there are other times when it would be more efficient to buy. A claims specific CDP is already trained to provide insights on various types of claim specific data and documents, such as:

• Lab results, which are highly technical and wide-ranging documents that show how data is recognized, extracted, and summarized. Lab results give you peace of mind over how your data is used and maintained, which is especially important with sensitive patient information.
• Data management. Claims have a complex life cycle with key events and different stages. Each stage is tied to specific dates, and an effective data management platform can ingest and chronologically order documents to work within them – creating a clear timeline to assist with decision making.
• Medications such as prescriptions and dosing changes are a unique feature of healthcare and health insurance applications. Industry trained CDP models are trained to use prescription data, identify changes, and provide your team with up to date information, ensuring no key information is missed.
• Configurability. An automated CDP is customizable, but it’s also pre-trained on millions of data points relevant to your industry. This can save years of development and costs compared to building your own to fit your workflows. Pre-developed CDPs can automate workflows across document types that you’re already using, such as medical records, legal reports, or handwritten documents. The most robust existing CDPs can configure workflows to meet your needs, ensuring your processes remain the same and teams adopt quickly.
• Dynamic data surfacing. Traditional claims processing systems often present data as ‘one size fits all’ but this can overwhelm users with less than relevant information. Whether it’s a high level medical record summary for a legal professional or a detailed chronology for a claims adjuster, a CDP platform can help meet each specific use case efficiently. 

While building a CDP may fit some scenarios, in many cases it’s smarter to configure one that’s already tested and trained. As AI fatigue sweeps the market, many in-house projects are stalling—often because teams underestimate the resources and expertise required to see them through. CIO recently noted the trend: “After thousands of failed AI pilots, the market has shifted: 50% of companies built their own AI tools in 2023, but only 20% were still trying by the end of 2024 as CIOs turned to proven commercial solutions.”

Ultimately, the priority is finding a solution that aligns with your organization’s unique needs, one that can deliver value without adding unnecessary complexity. At Wisedocs, we’re committed to being that partner by equipping claims teams with the insights they need and the right questions to ask when evaluating CDP providers.

To learn more, check out the Wisedocs 2025 Buyer’s Guide for more details on finding a Claims Documentation Platform to fit your needs.