In AI utilization of unstructured data

• Internal documents are scattered, and a significant amount of effort is required to prepare the data before using AI.
• The data is updated frequently, making it difficult to keep the RAG dataset up-to-date.
• The document structure (headings, paragraphs, tables, etc.) is broken, and it cannot be formatted in a way that AI can correctly understand.
• The quality of the preprocessing is inconsistent, which prevents improvements in search accuracy and hinders the progress of the proof-of-concept (PoC).
• Everyone wants to process data with the same quality, but it's currently dependent on the engineer's skills.

Unstructured is a solution that automatically converts unstructured data, which accounts for 80-90% of enterprise data, into clean, structured data that can be correctly handled by AI. It provides an integrated preprocessing process essential for using RAG and LLM, including complex layout analysis, OCR, chunking, and integration with vectorization.

Furthermore, as a no-code platform, it enables stable data formatting that does not depend on engineering skills. It can handle the processing of large files and keep up with updates, efficiently building a foundation for improved search accuracy and knowledge utilization. In addition, it is equipped with connectors for integration with various cloud services, allowing for flexible expansion of integration with existing systems and automation of operations.