Text and DocumentProcessing

Text and document processing involves the automated analysis and manipulation of   written content, such as documents, web pages, emails and social media posts. It allows users to glean helpful information and make decisions quickly and easily, supported by data.

In Snowflake, you can process documents (including PDF, Word, .txt and image files) by uploading files to the Document AI interface. Document AI uses a pretrained multi-modal large language model (LLM) to extract data from the document — it’ll even recognize graphical elements like handwritten text and logos. You can automate this process, so that Document AI completes data extraction any time you bring a new file Snowflake.

Text and document processing uses AI to automate the extraction and analysis of data from documents, such as emails, log files, PDFs and scanned documents. Large language models (LLMs) can analyze and summarize content, and that output can help both developers and business users perform tasks that would be time-consuming, arduous and error-prone if done by humans alone.

AI-powered text and document processing automates tasks that otherwise need to be performed manually, reducing costs and saving time. It can power a wide variety of applications such as support call summarization, marketing or sales sentiment analysis, or corporate report analysis. The benefits of using tools that automate text and document processing include:

By automating the analysis and organization of textual data, text and document processing can save time and effort compared to manual efforts, speeding up tasks like data entry, document summarization and document classification.

Automated text and document processing systems can analyze high data volumes with greater precision than manual efforts, improving the quality of downstream decision-making.

By reducing time-consuming manual labor, organizations can reduce their overall operational costs.

By analyzing and deriving value from a greater volume of textual data, teams can more easily identify trends and drive higher confidence in decision-making, which can provide a competitive advantage.

Text and document processing can enable faster and more accurate responses to customer inquiries, automate customer support processes and provide better tailored personalized recommendations.

Organizations can use text and document processing to analyze social media posts, customer reviews and survey responses that might provide a more detailed, comprehensive view of how customers feel about their products, service offerings and more. Being able to determine customers’ emotions and opinions quickly can help refine marketing strategies, support product development and even determine market fit.

Being able to automatically identify and flag non-compliant content allows organizations to more easily address compliance requirements. They can also monitor and mitigate risks by analyzing text data for potential threats or suspicious activities.

Thanks to its versatility, text and document processing can be helpful for essentially any department across industries — especially ones that have a significant amount of written content to manage. Some examples include:

Lawyers, paralegals and legal secretaries can use text and document processing for contract analysis, legal research and e-discovery. This helps firms and legal departments automate document review, lower costs and improve the accuracy of legal work.

Automating things like ticket classification or sentiment analysis can help organizations provide faster and more precise support, which can contribute to improved customer satisfaction.

Resume screening, employee feedback analysis and policy compliance monitoring are a few examples of how text and document processing can help HR departments streamline workflows and make more informed decisions.

When organizations understand customer preferences more deeply, they can create more effective marketing strategies and craft more engaging content for their campaigns. Text and document processing can aid with this by providing sentiment analysis and content optimization.

End users interact with the knowledge base, typically through a chat interface or question-answering system.

Relevant data sources are aggregated, governed and continuously updated to provide an up-to-date knowledge repository. This includes preprocessing steps like chunking and embedding the text.

A vector store maintains the numerical representation (embeddings) of the knowledge base. Semantic search is used to retrieve the most relevant chunks of information based on the users’ query.

If there isn’t enough data to adequately train machine learning models, the accuracy and reliability of performance may suffer.

Text data can contain errors, typos or irrelevant information (“noise”) that can affect how accurately the model processes and analyzes it.

With the increasing complexity and size of language models, scaling can be a challenge. Building scalable text processing solutions that can handle large, complex datasets while maintaining high performance remains difficult.

Processing text data may involve handling sensitive information, such as when a healthcare provider is using it to summarize medical records that contain patient identifying information. Organizations must be sure to comply with privacy regulations and carefully evaluate ethical considerations.