AI Solutions
for the Future of Business
AI Platform
HIPSTO’s pioneering, end-to-end AI platform empowers a range of AI applications for accessing text data.
Our no-code pipelines, underpinned by the latest Transformer architectures, help smart global businesses stay ahead of the data explosion.
Learn more about our tech stack and its applications.
FalconV™
Our FalconV™ AI stack powers a fully-automated, multilingual, end-to-end solutions platform.
Delivering a smooth, seamless integration of data extraction, curation, analysis and delivery, its power is untouched by other global platforms.
All proprietary AI microservices have been developed using the very latest Transformer architectures (e.g. mT5) to enable advanced multilingual – 100+ languages – text analytics via Natural Language Understanding (NLU).
Blind Vision
Blind Vision is simply the world’s leading web text extraction and automated labeling technology.
We’ve combined the power of Raw Code Processing and Computer Vision to create a global paradigm shift in analysing text data.
Blind Vision is now part of the input layer of our larger no-code AI text technology platform, providing continuous clean text data, and empowering our market-leading curation and analysis layers.
Natural Language Understanding
Natural language understanding (NLU) is a subtopic of the natural language processing (NLP) field of artificial intelligence.
NLU focuses primarily on interpreting or understanding the text, and it is typically based on matching the parsed input to an underlying knowledge model or structured ontology.
Multilingual Model
One of the most remarkable things about HIPSTO’s FalconV platform and the underlying native Blind Vision technology that powers it is our highly accurate Natural Language Understanding of over 100 languages.
We deliver ground-breaking web scraping, sentiment analysis, text classification and other intelligent services across all the world’s major languages, all without sacrificing levels
of accuracy.
Discover the World’s Leading AI Text Technology Solutions
Discover how HIPSTO can help you find transformative data insights
Blind Vision
We have dubbed our proprietary web text data extraction and labeling technology, Blind Vision. It is now, arguably, the best in class globally and superior to the established and trusted Computer Vision and other technologies.
Test studies* have been conducted versus a US based web data integration platform that uses Computer Vision, and the results are conclusive. They showed a +35% increase in accuracy and -65% lower running costs using Blind Vision technology.
We like to remain a little secretive about the ‘sauce’, but we can say that Blind Vision combines sophisticated Raw Code Processing algorithms and our own deep learning network architecture.
Advanced Sentiment Analysis
Sentiment analysis is a very powerful tool with many commercial applications. However, it is very difficult to do well. Many claim to have a sentiment solution, but upon analysis, few in the market really do. Current solutions suffer from poor consistency, limited accuracy and lack of advanced deep learning techniques.
Humans can easily judge the polarity of text, unlike machines. We have developed an apex sentiment tool, using our proprietary neural network architecture, which enables real Natural Language Understanding (NLU) and emulates how humans judge the content and context of text.
Our solution provides consistent sentiment analysis of high or low-frequency content, in long or short format, across 100+ languages. And, we can do all of this with an impressive F1 score of 0.9443!
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Named Entity Recognition
Valuable (business) information is buried in a largely text-based (79%) data explosion, most of which also resides in unstructured data on the web. The ability to extract, organize, analyze and connect large amounts of unstructured text data has become of paramount importance.
Extracting, classifying, and connecting entities via Named Entity Recognition (NER) technology plays an important role in sorting unstructured data and identifying valuable information. NER is a key foundational block for any information discovery pipeline and the basis for most Natural Language Processing (NLP) solutions.
We have built the new industry standard: Multilingual NER (in 100+ languages) that is unrivaled in accuracy vs. current ‘open source’ solutions and performs with an F1 score of 0.95.
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Web Scraping
Web Scraping may sound easy, but it’s not! We have solved the 5 most prevalent issues faced by standard web scraping methods.
One key issue involves constant website layout changes. SEO improvements and UX/UI changes are delivered through HTML layout amendments. As a result, element locators that web scrapers are configured to in order to extract data, change and break the scraping process by extracting incorrect or no data. It takes a lot of manual effort to update these configurations and maintaining thousands of sources becomes near impossible.
We have fully automated the process of source reconfiguration to present you with a truly scalable, leading-edge web scraping solution. One that operates in 100+ languages and can scrape any text data from any web source in real-time.
Related Content Detection
Discovering and determining how to treat ‘similar content’ is one of the fundamental challenges in Natural Language Processing (NLP). This problem of semantic similarity becomes more complex when the automatic search, retrieval and analysis process is focused on multilingual text content from several web sources.
Our engineering and data science team have released 2 science papers detailing how to use and extend the current leading Transformer models, then apply them to tasks focused on the semantic similarities of news content.
We are now able to find any related content in 10o+ languages, cross-lingual, and irrespective of the input text length of the content. A global breakthrough in the field of NLP science.
Automated Text Classification
We have built an industry leading, multilingual, automated text classification capability that demonstrates superior accuracy (underpinned by Natural Language Understanding), uses no language translation layer (which significantly distorts the meaning of content) and is able to proces all length content, via our single, one stop shop, platform.
These accuracy levels now mimic human understanding of any text.
BERT Semantic Similarities
Research into the possibilities of using the multilingual BERT model for determining semantic similarities of news content.
Semantic Similarity of
Arbitrary-length Text Content
Research on the specific features of determining the semantic similarity of arbitrary–length text content using multilingual Transformer based models.