If you've tried using popular LLMs like Claude or ChatGPT for document OCR and data extraction, you’ve likely been impressed by their ability to understand context and handle variable layouts. However, their tendency to hallucinate can result in critical output errors, making LLMs risky for data extraction automation without robust detection mechanisms. Cradl AI solves this by combining top-tier LLMs with proprietary AI models to provide 99.9% hallucination-free data extraction, enabling you to confidently use LLMs for OCR tasks without worrying about inaccuracies while also providing features to automate workflows for efficiency and scalability.
Software Engineer
Large Language Models (LLMs) have significantly advanced beyond traditional Optical Character Recognition (OCR) methods, offering the ability to understand context, work with various document formats, and provide more accurate results with a single AI model. This ease of use and versatility makes LLMs an attractive option for automating document data extraction. But here's the catch: LLMs are notorious for "hallucinating", which refers to the AI generating incorrect or fabricated information.
While hallucinations may be manageable in content creation tasks like generating emails or other text content, they pose a serious risk when used for structured data extraction from documents like PDFs. When tasked with extracting specific fields, such as dates, amounts, supplier names, etc., LLMs may introduce errors that are really hard to detect because hallucinations often sound credible but are ultimately wrong.
In data extraction, where you're typically looking to extract between 5-20 distinct fields from each PDF, these errors become much harder to spot. In financial, legal, or healthcare document processes, these kinds of inaccuracies can lead to severe consequences, ranging from lost revenue to legal ramifications.
Data extraction is about accurately locating and extracting existing data, not generating it.
It’s important to understand why hallucinations by powerful LLMs like Claude and ChatGPT aren’t going away anytime soon. At their core, LLMs generate text based on patterns and probabilities. They don’t “understand” content in the same way humans do; instead, they predict the most likely continuation of a prompt. This makes it challenging to guarantee the absolute accuracy of their outputs, especially when dealing with structured data extraction, where precision is paramount.
The problem with hallucinations is that they can appear quite realistic, even though they’re ultimately incorrect. Without a robust mechanism to detect and correct these hallucinations, LLMs remain too unreliable for critical document workflows. In short: LLMs will continue to hallucinate unless a solution is put in place to mitigate or prevent these errors.
At Cradl AI, we’ve solved the hallucination problem in LLM-powered document data extraction. By combining the strengths of top-tier LLMs like Claude with proprietary AI models and advanced algorithms, Cradl AI is able to minimize the risk of hallucinations to less than 1%, meaning that businesses can confidently use LLMs without worrying about errors creeping into their data extraction processes.
Cradl AI provides all the benefits of LLM data extraction while eliminating the risks of hallucinations. By combining the power of LLMs with custom models tailored specifically for document processing, Cradl AI offers accurate, reliable, and hallucinatory-free data extraction across any document type.
One of the key features Cradl AI brings to the table is the use of confidence scores to assess the accuracy of the LLM's output. This allows us to flag predictions that are uncertain or potentially erroneous, and automatically route them for human review. This human-in-the-loop approach ensures that when the AI isn’t sure, the right checks and balances are in place to correct any mistakes before they become problematic.
The flagged predictions that undergo manual review help to refine and improve Cradl AI’s understanding of your documents. Every correction feeds into the system, continuously enhancing the AI’s ability to handle increasingly complex document processing tasks. This cycle of continuous learning ensures that the more documents the AI processes, the more accurate it becomes.
Whether you're a small business looking to automate basic data extraction tasks or an enterprise processing hundreds of thousands of documents per month, Cradl AI is designed to scale with your needs. Its no-code platform allows businesses of all sizes to implement automation without needing specialized expertise. You can focus on more important business tasks while Cradl AI takes care of accurate, automated data extraction to reduce manual data entry.
We’ll help get you started with your document automation journey.
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