Document Management 4.0

Chaos in the File Cabinet

Document Management 4.0

AI solutions like Semantic Document Search help companies provide relevant data and information to their employees quickly and centrally.

Today, there is a trend of exponential data growth. In 2012, we entered the Zettabyte era for the first time, meaning the total amount of digital data worldwide exceeded 1,000,000,000,000,000,000,000 bytes. By 2022, approximately 100 zettabytes of new data are being generated per year.

17. October. 2024

Christian Weber

CTO, AI Expert & Founder

Karin Schnedlitz

Content Managerin

Smartly Addressing Challenges

The Literal Search for a File in the Data Pile

This presents companies with the challenge of finding relevant (1) information in a timely manner, which is further complicated by factors such as identifying synonyms, answering specific questions, and determining (semantically) similar results

Traditional keyword searches, commonly used in most document management systems today, are overwhelmed by these requirements (2). They prioritize documents based purely on statistical properties, not contextual relevance. As a result, important documents often rank lower in search results during a traditional keyword search.

For those searching, these documents become less interesting, and for companies, this can be disastrous, as the target audience may not find important information in a timely manner, or the company must invest significant manual effort to counteract the issue. AI-based solutions can provide valuable results here. A Semantic Search interprets the context of a query, aiming to understand the intent behind it in all its facets. This allows it to correctly classify cases and automatically deliver results with a relevance score.

Semantic Search in Pharma

The Search Engine That Understands Us

This can be well demonstrated through a common use case specifically for the pharmaceutical industry—a real-life example from Leftshift One. In an international pharmaceutical corporation, there is a vast collection of scientific literature, data sheets, and SOPs (Standard Operating Procedures).

These documents are available in various formats and are only partially searchable (e.g., scanned documents). Employees want to search this collection centrally and receive relevant excerpts from these documents.

Solving Tasks in Record Time

This is where Smart Document Search comes into play: text is extracted from all documents (using Optical Character Recognition, if necessary, to convert scanned documents into machine-readable text). The extracted texts are converted into vectors and stored in a search index. To further enhance quality, the search index ranks the top results using a Cross-Encoder, which we have specifically trained on question-answer pairs.

User inputs can now be questions such as “How do I report an incident at Plant XY?” or “How long must delivery notes A38 be retained?” In the second case, the search retrieves the exact section of the document that specifies the retention period from all documents dealing with delivery note A38.

Semantic Search thus saves a significant amount of time that employees would otherwise spend on manual searches or dealing with inaccurate search results. The system also continuously learns and improves the search over time.

More Information

Notes in the text:

(1 ) Relevant refers to the specific use case or search query, making it context-dependent.
(2) Both the search query and the potential search result must contain identical words for a match to occur.

Did you enjoy the article? Discover our AI solution, Cognitive Document Automation, and find out how you can also benefit from Semantic Search.

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