Automated Document Validation: A Major Leap Forward

For decades (yes decades) document validation in Life Sciences has largely relied on human reviewers. While this approach is inefficient, imprecise, and time-consuming, it has consistently outperformed technology-driven solutions. This is largely because validation involves a variety of different tasks—some straightforward, others deceptively challenging, and many requiring complex human reasoning.

Why isn’t there an automated solution?

Although technology has improved many aspects of document creation, it failed to address the more challenging and complex parts of validation in a scalable and cost-efficient way. These parts require a combination of rules, the ability to understand data within a document, and the skill to dynamically use information from many sources to assemble validation criteria.

Consider the seemingly simple task of verifying that all in-document references to tables, figures, and graphs are correct, accurate, and properly formatted. A human reviewer can identify and cross-check references to “Graph 1,” ensuring its presence while also confirming that font styles, sizes, and spelling are consistent and correct. This has proven a challenge for software to perform on different documents and at scale.

What’s changed?

The combination of high performance GPUs and the emergence of Large Language Models (LLMs) offer an opportunity to rethink traditional automation approaches, providing dynamic, scalable, and cost-effective solutions.

Today’s validation software, aided by these advancements, can create personalized validation criteria for a company and its projects. Extracting the criteria to validate, in real-time, from documents and assembling one-time use validation rules. Rules that test contextual standards compliance.

Key Takeaways

• Automated document validation technology is now a reality. And companies that embrace an automation-first approach will benefit with faster processing times, fewer errors, and reduced costs.
• Computers can now tackle the example “Graph 1” validation task described above with ease. And software providers can enable predictable outcomes for a fraction of previous costs.
• Buyer beware. With the influx of “AI” labeled solutions in the market, it’s essential to choose wisely. Be wary of vendors who are unwilling or unable to explain the inner workings of their solutions. While no company will disclose proprietary information, a lack of transparency or an unwillingness to demonstrate your use cases could indicate limitations in the solution’s effectiveness.