Three-quarters – or more – of potentially useful information in the business world is in an unstructured format, and that’s not going to change anytime soon. Humans are hard-wired to create content in natural language, rather than charts, tables or code. Problem is, unstructured content is only available to those who take the time to read it.

Meet ayfie Inspector, a revolutionary text analytics platform makes that knowledge accessible to analysis and prediction algorithms by unearthing the semantic structure behind the text. Basically, ayfie Inspector reads like a human … only light years faster. Here’s how.


Documents can be collected from almost any common storage system or application, from cloud storage to ECM repositories to information stored in CRM systems and historical databases. Documents can be uploaded directly or pushed through an API. After collection, documents are converted into a common text representation, keeping all metadata and structural information intact. Typically, this takes a few seconds.

ayfie benefits from over a decade of experience in the enterprise search market where connecting to many different systems – from content management to email servers – is key to success. Over 70 connectors are available and can be extended using a robust SDK. Text and metadata can be extracted from all popular document formats. Text contained in images is extracted using optical character recognition (OCR) technology.

ayfie Inspector enriches text by applying large electronic dictionaries that classify information about semantic and syntactic properties of words. Using base forms, synonyms, spelling variants and dependency properties for all words, ayfie finds and annotates all external structure that exists in documents and makes it accessible for further analysis. Traditional text analytics platforms treat letters and words like symbols; ayfie Inspector understands their underlying context and meaning.

ayfie Inspector builds on more than 30 years of research in compiling large scale electronic dictionaries and other linguistic resources. Inflectional forms, synonyms, and other phenomena (such as decomposition in Norwegian and German) are handled for all major European languages.

By analyzing the semantic structure inside documents, ayfie Inspector organizes unstructured content, making it more efficiently consumable for humans and formal enough for machines.

"What distinguishes ayfie from all previous and current approaches to text analysis is our view of the basic elements of meaning in language. Our algorithms do not simply manipulate isolated words – which are always either strongly ambiguous or extremely vague – but complex semantic constructions which express meanings at a higher level. We apply combinations of very large semantic dictionaries that encode a lot of information about the entities mentioned in the texts together with millions of semantically typed parsing rules that know how relations are expressed syntactically. We are therefore able to extract the names of entities, the facts and the opinions expressed in the text."

Franz Guenthner, professor of Computational Linguistics at the Center for Information and Language Processing at the Ludwig-Maximilian-University (LMU) and Technology Advisor at ayfie Inc.

For instance, there are thousands of ways to describe the acquisition of one company by another. ayfie Inspector recognizes them all, normalizes them and makes them accessible for search, analysis and visualization.

Word inflections continuously lead to inconsistent search results on Google.

This effect is of course multiplied for multi-term queries.

In the same manner, doctors use many different expressions, ranging from colloquial to very formal, in describing obesity in patient notes. ayfie understands all of these variations and can map them to the correct ICD 10 code.

For dealing with structure extraction and semantics, ayfie Inspector builds on well-researched linguistic frameworks based on the works of Zelig Harris, Maurice Gross and Franz Guenthner. This sound theoretical foundation is combined with our blazingly fast proprietary extraction engine and exhaustive resources in many languages and domains.

Because we utilize highly scalable cloud architecture, our storage and processing capabilities are essentially endless – from indexing thousands of documents for an eDiscovery case, to analyzing thousands of scientific articles from the biggest publishers in the world.

Supporting both graph-based and search-based access patterns, ayfie Inspector efficiently executes different types of algorithms on the extracted information and raw data. 

ayfie uses a solid architecture that scales horizontally to any content size, combining open source innovations and proprietary procedures.

ayfie Inspector's ability to extract and structure information from unstructured content makes the platform uniquely suited to power specific applications.

Text suggest (or "type ahead") is easy when searching a structured product catalog or database. ayfie makes it possible when querying unstructured content, like during the eDiscovery review process or complex enterprise search applications.

ayfie can automatically extract persons, locations, organizations, key phrases and many more entities out of the box that are perfect ”hand rails“ into the content to be searched.

ayfie Inspector's focus on context and understanding allows users to search using natural language, rather than arcane strings of commands and out-of-context keywords, without sacrificing relevancy.

For instance, the query:

matches this document content:

when processed by ayfie, because the platform reduces both sentences to a meaning representation of:


and are thus considered a match of each other.

By aggregating extracted predicates of the same type, ayfie Inspector can build tables of events or facts in any domain required. For example, a large industrial manufacturer uses ayfie Inspector to analyze incident reports about turbine malfunctions. Service personnel write their reports in natural language, and ayfie Inspector turns them into structured, tabular content:

Ring Position Finding Recommendation
Xla2 16 Crack Replaced
Xla3 7 Wear Smoothing

Based on the tables created by ayfie Inspector, the manufacturing company can now precisely answer questions like:

"Which part failed most often because of outside heat?"

"Which malfunction is the most likely for a certain class of parts?"

"Which environmental conditions are the most detrimental to the overall reliability of the component?"

Thus, all free-text service reports and even historical texts can be subjected to rigid analyses that were once impossible without time-consuming and expensive data transformation projects.

Traditional machine learning approaches to text analytics are capable of yielding fantastic results ... but only after massive amounts of training data are passed through the algorithm.

ayfie Inspector minimizes training time by breaking text into tokens, finding the boundaries of sentences, dealing with synonyms and inflectional forms, and extracting salient phrases and entities. This produces a more structured – and machine-readable – form of content than raw analysis.

Thanks to this structure, learning algorithms can process data more easily by looking for patterns and trends. For instance, clustering and categorization yield higher precision when ayfie Inspector’s structured representation of the text is used instead of simple token vectors. This produces a higher gain in precision than using a better learning algorithm on the unstructured text.

ayfie comes with a wide range of data visualization and analysis tools. It can also be integrated with third-party BI platforms.

We solve business problems with data analysis tailored to your needs by leveraging natural language processing, guided machine learning, linguistic analysis and years of experience. Ready to start seeing value from your content?

Contact Us Today

— an ayfie whitepaper