The amount of data generated and received by businesses has become staggering. Businesses commonly lose several pieces of relevant data in documents due to the time-consuming nature of data extraction.
Document artificial intelligence (AI) is at the forefront of document processing solutions. They enable businesses to comprehend document content more efficiently outpacing manual methods.
The primary goal of document AI is to convert unstructured documents into structured data.
Before we delve into the technical aspects, let’s understand the power of LLMs in document processing.
Power of LLMs in Document Processing
Large language models (LLMs), a type of deep learning model, use natural language processing (NLP) to mimic human-like text understanding and generation. They use vast amounts of training data to gain a strong contextual understanding of various domains.
This makes them particularly useful for applications like document processing technology, where a deep understanding of language nuances is vital.
How LLMs Affect Document Processing
LLMs can be trained to extract specific pieces of information from documents, including:
- Invoice numbers
- Contract terms and conditions
- Product descriptions
In addition, LLMs can go beyond keyword extraction. They can understand the semantic meaning behind phrases, which helps the model check that the extracted data has contextual relevance.
The LLM can categorize the document’s content accurately by analyzing and understanding it. This includes distinguishing between invoices, memos, and contracts. LLMs can also categorize documents based on their topic, date, or any other key variable.
This means businesses can improve their document management, ultimately resulting in better workflow management efficiency. When businesses are dealing with thousands of documents, this is particularly useful. It also helps with document retrieval, such as accessing contracts related to a specific client.
While both general and custom LLMs have their merits in document processing, it’s essential to discern which is more suitable for specific applications.
Benefits of Automating Document Processing with AI
With the market moving so quickly, businesses must find new ways to adapt. Using AI for document processing presents various advantages that help businesses keep up.
Speed and Efficiency
Typically, a human would take several hours or days to process many documents. One clear benefit of using AI for document processing is the ability to speed up this process drastically and process data from many documents in minutes or seconds.
As a result, businesses can improve their efficiency by freeing up their employee’s schedules so they don’t need to spend time on document processing. Instead, they can focus on more demanding tasks that require human creativity. Moreover, it also helps improve efficiency by integrating AI into existing workflows.
This helps reduce operational costs since the number of manual hours is significantly reduced. Businesses can spend less on labor for document processing.
Accuracy and Reliability
Manual document processing is always prone to human errors, including misreading, misinterpretation, or data entry errors. Once the AI system has been trained with suitable training data, it can accurately extract information from documents.
Additionally, AI systems can ensure that data extraction is consistent, regardless of the volume of documents. Carrying out manual document extraction with too many documents would often result in delays or higher chances of errors.
Unstructured documents usually pose a challenge regarding accurate data extraction when performed manually. With AI, this can be carried out with high accuracy, regardless of the unstructured data.
Since AI systems have built-in validation mechanisms, they can cross-reference extracted data with other sources to check accuracy and validity.
Customization and Flexibility
Businesses often have specific needs and requirements that their LLM needs to be able to meet. More often than not, general LLMs aren’t able to fulfill these requirements due to their lack of specialization.
By fine-tuning the model to recognize certain layouts or terminologies unique to a certain industry, the model can be made more tailored to the organization’s requirements. This means the model will be better suited for dealing with documents in that specific industry.
Notably, AI systems can process various types of documents. This means businesses don’t need a different tool for document types and can use one primary tool to process documents.
Some of the different types of documents that AI can extract data from include:
- Bank statements
- Legal documents
- Performance reports
After considering the benefits of document AI, we will now consider the more technical aspects of how it works.
The Inner Workings of Document AI
The technical process of document AI has five main stages, which can be seen below:
Let’s explore how each stage of document processing works.
1: Data Capture
After the documents have been uploaded to the AI model for processing, the relevant data is captured from each document. This involves using techniques like optical character recognition to capture text from scanned documents and image recognition for any visual data.
After capturing the data, documents or data chunks are categorized based on certain criteria. The AI model will identify the document’s type, which could be an invoice, memo, or contract.
Specific pieces of important information are then pulled from the classified documents using techniques like pattern recognition or keyword searches. This includes extracting information like names, dates, or other specific information.
The extracted data is enhanced by adding context by linking related data or filling in gaps. This could involve cross-referencing extracted data with existing databases to add more detail. An example would be linking a person’s name extracted from the document with their profile in a CRM system.
5: Review and Validation
A review process takes place to check the accuracy of the extracted data. This can be done by a human expert or by using algorithms to cross-check the data for any inconsistencies or errors.
The final step then occurs, where the extracted data is validated and confirmed to be ready for use. All the data is checked to see if it meets the required criteria and is in the correct format. Any errors are brought up so the issue can be resolved by sending the data back for review.
Features of Document AI
Document AI has various features that make it more applicable to business workflows than general AI, including the ability to build customized models and API integration.
Instead, custom models offer a more specialized solution to businesses that need the LLM to be well-adjusted to a particular domain or industry. For example, an LLM trained on legal documents will be better suited to recognizing legal terminologies and structures, making it more efficient at processing contracts or legal briefs.
On the other hand, an LLM trained on medical records will be more accurate in extracting data like patient information, diagnoses, and treatment details.
LLMs can also be trained for specific types of documents rather than just different domains. The LLM used for document processing can also be specifically trained to process particular types of documents. For example, if a business wants to focus on processing invoices, the LLM can be trained using other invoices as training data for improved results.
Another standout feature of document AI is its ability to seamlessly integrate with existing software and platforms through application programming interfaces (APIs). As a result, businesses don’t need to make massive adjustments to their current systems.
They can easily add the document AI solution into their existing workflows, which lets them carry out real-time processing and analysis of documents. This includes integrating with a customer relationship management (CRM) system to update customer records or an enterprise resource planning (ERP) system to process invoices.