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Cortical.io announces the first application of real-time Semantic Supercomputing based on NLU

12 Nov 2019

Cortical.io announces the first application of real-time Semantic Supercomputing based on NLU

  • Introduces high-performance enterprise message filtering and routing appliance-powered by Xilinx Alveo accelerators

  • Announces partnership with Supermicro Computer

SAN FRANCISCO and THE HAGUE November 12, 2019 – Cortical.io, a leader in AI-based Natural Language Understanding (NLU) solutions, today announced the debut of a new class of high-performance enterprise applications based on “Semantic Supercomputing.” Semantic Supercomputing combines Cortical.io AI-based NLU software inspired by neuroscience with hardware acceleration to create new solutions to understand and process streams of natural language content at massive scale in real time.

“Ever-increasing unstructured data is overwhelming the world and the available processing power and current statistical approaches to deal with it,” said Francisco Webber, co-founder and CEO of Cortical.io. “We are taking the concept of supercomputing to the next level with the introduction of Semantic Supercomputing and the ability to deliver real-time processing of semantic content.”

First Application of Semantic Supercomputing Tackles Enterprise Messaging

The first application of Semantic Supercomputing, a Messaging Classification Appliance that can filter, classify and route streams of messages in real time by understanding the semantic content – the meaning and intent of the messages – was unveiled today at Xilinx Developer Forum (XDF) Europe keynote session at the Xilinx, Inc. developer conference held November 12-13 in The Hague.

Building on the strategic relationship announced with Xilinx at last month’s at XDF Americas in San Jose, Cortical.io is developing this first of a series of FPGA-based appliances powered by Xilinx Alveo accelerator cards. Cortical.io also announced it is partnering with Supermicro (SMCI), a global leader in enterprise computing, storage, networking solutions and green computing technology, to deliver the email solution on a pre-loaded server. The appliance will enable enterprises to filter and route massive volumes of email messages in real time with high precision and recall based on the meaning of the message. The product will be available in Q1 2020.

“The goal is to reduce the wasted efforts of handling irrelevant or misdirected emails by first line business operations – including support, sales, purchasing,” said Cortical.io CMO Steve Levine. “The appliance will be able to handle a massive volume of messages daily in real time.”

Enterprise system administrators will be able to train the system and customize the filtering and routing based on a small number of sample emails. Once trained, the appliance works across multiple languages (English, Spanish, German, Portuguese, Cantonese, Arabic, French, Italian, Mandarin Chinese, Dutch).

“The demand for real-time AI services has never been greater and, together with Cortical.io and Supermicro, we’re excited to be building a solution for solving the incredible processing challenges of real-time Natural Language Understanding on a massive scale,” said Adam Scraba, director of marketing, Data Center Group, at Xilinx. 

“Supermicro’s proven and extensive server portfolio when combined with Xilinx Alveo accelerators and the Cortical.io AI-based NLU software, delivers a sophisticated enterprise platform to address the growing data explosion, especially email,” said Don Clegg, senior vice president, Worldwide Sales, Supermicro.

This is just the first instance of using the power of Semantic Supercomputing.  Cortical.io CEO Webber stressed, “Our goal is to make possible the broad implementation and deployment of AI solutions for automating business processes and solving the most challenging use cases that depend on human understanding, decision making and execution.”

The unique Cortical.io approach to NLU is inspired by the latest findings on the way the brain processes information. This approach provides advantages over traditional machine learning methods and helps businesses solve many open NLU challenges like meaning-based filtering of terabytes of unstructured text data, real-time topic detection in social media, or semantic search over millions of documents across languages.

Learn more about Semantic Supercomputing here and find more details about Cortical.io’s Messaging Classification Appliance here.

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Contract Intelligence 3.1.0 Released

15 Oct 2019

Contract Intelligence 3.1.0 Released


Key New Features and Improvements in This Release include:

  • Improvements for detecting and converting source data:
    The conversion of table data has been improved, as well as the reproduction of source document styling, which facilitates document navigation and comparison
  • New logic for defining relationships between extraction targets:
    Relationships can be created between multiple extraction targets which enables extractions to be grouped for easier interpretation.
  • New Export to CSV functionality from the Documents tab:
    Users can now download a .CSV file that contains a list of document attributes, including Annotation/Extraction values, their respective locations in a document, and associated Confidence scores.
  • Filters added to the Extractions and Annotations columns:
    To make it easier to group and view documents based on extraction and/or annotation criteria, filters have been added to the Documents page that allow you to select Extraction and Annotation ranges for documents.
  • Improved organization for Document type folders:
    Sub-folders can now be created and all folders can now be renamed in the Documents view.

Contact us to learn more about all new features!

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Cortical.io Collaborates with Xilinx to bring Natural Language Understanding Supercomputing to Enterprise Applications

01 Oct 2019

Cortical.io Collaborates with Xilinx to bring NLU Supercomputing to Enterprise Applications

Receives Strategic Investment from Xilinx; Demonstrates its AI-Based Technology at Xilinx Developer Forum


Cortical.io, a leader in AI-based Natural Language Understanding (NLU) solutions, today announced a strategic relationship with Xilinx, Inc. to deliver next generation machine learning solutions to unlock the value of enterprise data.

Cortical.io will unveil its NLU technology running on Xilinx Alveo accelerator cards demonstrating orders-of-magnitude performance increases over standard computing platforms during the Xilinx Developer Conference in San Jose, Oct.1-2. Francisco Webber, Cortical.io CEO and co-founder, also announced that Cortical.io has received a strategic investment from Xilinx and will use the additional capital to expand its ability to deliver innovative NLU-based applications to enterprise customers. Terms of the investment were not disclosed.

The unique Cortical.io approach to NLU is inspired by the latest findings on the way the brain processes information. This approach provides advantages over traditional machine learning approaches and helps businesses solve many open NLU challenges like meaning-based filtering of terabytes of unstructured text data, real-time topic detection in social media, or semantic search over millions of documents across languages.

Webber will also deliver a talk at XDF on “Semantic Supercomputing” – to discuss how the combination of hardware and software enables a new class of high performance NLU applications for the future.

“Cortical.io NLU solutions running on Xilinx Alveo accelerator cards will enable appliances that deliver unmatched performance for demanding enterprise applications,” commented Thomas Reinemer, COO of Cortical.io.  “As FPGAs become common acceleration platforms in the fast-evolving computing environments of modern data centers alongside CPUs, Cortical.io innovative solutions will take full advantage of the power of the Xilinx FPGA-based accelerator cards.”

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Cortical.io Contract Intelligence wins AI TechAward

23 Aug 2019

Cortical.io Contract Intelligence Wins AI TechAward

Practical Application of AI Saves Time and Money

We’re pleased to announce that Cortical.io Contract Intelligence has won the 2019 AI TechAward in the category: Natural Language Processing (NLP) Technology. The 2019 AI Tech Awards celebrates technical innovation, adoption and reception in the AI, Machine Learning & Data Science industry and by the developer community. 

Cortical.io Contract Intelligence uses patented Natural Language Understanding technology to understand the meaning of whole sentences, paragraphs and provisions. This technology provides the capability to accurately extract and classify information in contracts. Other solutions using traditional Natural Language Processing (NLP), based mainly on keywords and statistics, have limitations that impact performance when processing more demanding extractions, classifications and interpretation of clauses.

 “Cortical.io is a great example of the newest AI & Machine Learning technologies now allowing developers & engineers to build upon the burgeoning AI industry. Today’s cloud-based software and hardware increasingly runs on systems needing increased data and intelligence, and Cortical.io’s win here at the 2019 AI TechAwards is evidence of their leading role in the growth of the AI ecosystem,” said Jonathan Pasky, Executive Producer & Co-Founder of DevNetwork, producer of AI Dev World & the 2019 AI TechAwards.

The 2019 AI TechAwards received hundreds of nominations, and the Advisory Board to the AI TechAwards selected our product/technology based on three criteria: (1) attracting notable attention and awareness in the AI, Machine Learning & Data Science industry; (2) general regard and use by the developer & engineering community; and (3) being a leader in its sector for innovation. The 2019 AI TechAwards will be presented at the 2019 AI TechAwards Ceremony during AI DevWorld (Oct 8-10, 2019, San Jose Convention Center), the largest Artificial Intelligence, Machine Learning & Data Science conference with tracks covering NLP, Open Source AI, AI for the Enterprise, Deep AI, Neural Networks and more.

Contract Intelligence Webinar with Ovum and Cortical.io
September 25, 2019 | 11am EST | 5pm CET

Learn how to save money and time with our award winning solution!

More Info

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Contract Intelligence 3.0 Released

16 May 2019

Contract Intelligence 3.0 empowers Subject-Matter Experts to manage the whole data extraction process

The new release of Cortical.io contract analysis software, Contract Intelligence 3.0, features a new, easy-to-use interface that enables Subject-Matter Experts (SMEs) to control the whole training and information extraction process.

With Contract Intelligence 3.0, SMEs define extraction targets and inferences, upload documents and start the training system to automatically perform extractions without the need or cost of an AI expert.

The solution quickly adapts to new requirements and new document types. SMEs can easily define new extraction targets for current document types or train the system for other document types by leveraging what the system has already learned.


Extraction results are highlighted within the document on the left, and easy to review and confirm with a click-and-go interface on the right.


The mapping manager enables to easily review, modify, add or remove extraction and inference targets.

Download the Contract Intelligence 3.0 Fact Sheet

To learn more about Cortical.io Contract Intelligence, explore the Contract Intelligence Solution pages, or contact us for a demo.

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Cortical.io Contract Intelligence Highlighted by Ovum

08 Apr 2019

Ovum, one of UK’s top analyst firms, has just released a report about Cortical.io Contract Intelligence, recognizing its benefits applying Artificial Intelligence (AI) technology in the legal domain.

Based on patented technology, Cortical.io Contract Intelligence extracts key information from complex contracts and populates existing contract management software with that information. The difference with other solutions in the market results from the neuroscience-based algorithm used to process text, which solves the problems of language ambiguity, is quickly trained and enables a fast and transparent implementation process.

“One of the challenges in the legal world is that the vocabulary used in legal documents is rather narrow, and small differences in how these words are expressed can lead to significant contextual or semantic differences”, describes Michael Azoff, distinguished analyst at Ovum. “With their original approach that combines Numenta’s memory model and unsupervised machine learning algorithms, Cortical.io has created a powerful technology to process unstructured text data and extract meaning. At Ovum, we believe that Cortical.io Contract Intelligence deserves evaluation.”

Cortical.io Contract Intelligence processes all types of legal documents, including lease agreements, ISDA master agreements, bond indentures, and certificates. The solution analyzes the meaning, not just of keywords, but of whole sentences, paragraphs, and long text so that the problems of language ambiguity and vocabulary mismatch within and across documents are overcome. For example, “done deal” and “signed contracts” are recognized as very similar by Cortical.io Contract Intelligence, although they do not have any term in common.

Cortical.io Contract Intelligence is accessed via a simple user interface and does not require any specific training. It is designed for business users and does not require any AI experts or knowledge. The solution is already used by Fortune 100 companies to reduce manual review and data extraction time, as well as contract processing costs.

Download the Report

More information about Cortical.io Contract Intelligence  

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PwC joins forces with AI pioneer Cortical.io

13 Sep 2018

Alliance of competencies will help PwC’s clients turn their data silos into value

PwC Germany and Cortical.io have recently signed a joint business relationship agreement whereby PwC becomes partner of Cortical.io and develops natural language understanding solutions using Cortical.io’s technology.

A young tech company, Cortical.io has developed a unique natural language understanding technology that solves many challenges related to big text data. The novel, meaning-based algorithm is based on Cortical.io’s patented Semantic Folding methodology. It allows both high-precision and high-speed semantic text processing and can be applied to any kind of unstructured text data. The application fields are close to endless, and practically all verticals can benefit from this innovation.

“Nowadays, every business, whether small or large, collects overwhelming amounts of text data,” explains Francisco Webber, CEO and co-founder of Cortical.io, the high demand for new data processing solutions across all industry sectors. “Each business is confronted with at least one big text data issue: some need to classify products based on lengthy text descriptions, others must extract key information from complicated legal or technical documents. Most companies need help in interacting with their customers, answering questions, recommending products, and so forth. All need an automated, reliable solution that can be easily adapted to their particular use case and can deliver first results within a few weeks. This is exactly what Cortical.io offers,” comments Webber.

PwC is one of the leading auditing and consultancy organizations in Germany and provides support to clients of different sizes, in a wide range of sectors. “In order to create a real value for our customers, our solutions focus on innovative technologies, but only if they have proven their worth in practical applications,” explains Sven Fessler, Senior Manager Big Data & Analytics at PwC Germany. “Cortical.io fulfills both criteria: they have developed a completely new approach to text processing and have already successfully deployed solutions in multiple enterprise environments”.

Cortical.io’s semantic technology is currently in production at several Fortune 500 companies where it is integrated into existing software solutions, in very different contexts. Sascha Demgensky, attorney and auditor at PwC, who, together with Sven Fessler, initiated the partnership, comments: “Next to its disruptive character, Cortical.io’s technology is easily adaptable to any business domain and delivers prompt, impressive results. We are confident that, combined with PwC’s expertise, we can offer our customers intelligent solutions that will impact their bottom line sustainably”. Cortical.io’s solutions that already bring customers significant cost savings include semantic search and contract analytics.

“The partnership with PwC represents a strategic move for Cortical.io,” states Francisco Webber. “PwC’s focus on high quality, their many years of experience and the wide range of professional services they provide make them an ideal partner to spread our technology. Major innovations are coming ahead in the field of natural language understanding,” concludes Webber, “and you can be sure that Cortical.io and PwC will be major forces behind this disruption.”

About Cortical.io:

Cortical.io offers Natural Language Understanding (NLU) solutions based on Semantic Folding, a methodology that opens a fundamentally new perspective on the handling of big text data. Inspired by the latest findings on how the brain processes information, the Cortical.io Retina engine converts language into semantic fingerprints, numerical representations that capture meaning explicitly.

The uniqueness of the Cortical.io algorithm makes it possible to solve many open NLU challenges, like meaning-based filtering of terabytes of unstructured text data, real-time topic detection in social media and semantic searching through millions of documents across multiple languages.

The company was founded in 2011 and holds a broad general license for Numenta’s HTM technology. Cortical.io has offices in Vienna (Austria), New York and the San Francisco Bay Area.


In this document, “PwC” or “PwC Germany” refers to PricewaterhouseCoopers GmbH Wirtschaftsprüfungsgesellschaft, which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity. This document is for general information purposes only and should not be used as a substitute for consultation with professional advisors.

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How was Cortical.io Support Intelligence deployed at a Fortune 100 company?

07 Jun 2018


Major manufacturer of computer network equipment


Reduce by 50% the manual effort required to resolve support requests


  • Presence of much non-relevant text
  • Complexity of support cases
  • Varying vocabulary and ways of describing problems
  • Attempts with other search systems failed in the task of quickly and consistently matching up similar support cases; searching by keyword produced poor results


  • Unsupervised training of the Support Intelligence Engine so that it learns the vocabulary used in the company’s support cases and product documentation
  • Removal of non-relevant text from support cases
  • Automatic learning of new vocabulary in real time
  • Autosuggestion of topics for support agents, to standardize support case text


  • Relevant search results found more quickly than with previous search methods
  • 70% reduction in average handling time of support requests



Do you want to create an exceptional support experience for your customers too? Contact us!

Read more about Support Intelligence

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Cortical.io named a 2017 Cool Vendor in AI Core Technologies by Gartner

17 May 2017

Gartner’s report “Cool Vendors in AI Core Technologies, 2017” recognizes vendors behind AI core technologies “to help data and analytics leaders, in collaboration with their business counterparts, conduct early AI experimentations for quick, opportunistic wins.”

We are very proud to have joined the league of Cool Vendors selected by Gartner!

Gartner subscribers can access the report here.

Disclaimer: The Gartner Cool Vendor Logo is a trademark and service mark of Gartner, Inc., and/or its affiliates, and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
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Introducing the Retina Platform

20 Oct 2016

Cortical.io has wrapped its Retina Engine into an easy-to-use, powerful platform for fast semantic search, semantic classification and semantic filtering.

The Retina Platform is an ecosystem that offers access to the core Retina Engine via the Retina Library (formerly called Retina Spark) and the Retina API. Both enable fundamental operations on text like text comparison, keyword extraction, segmentation or context identification. The Retina Library includes additional modules for semantic search, dynamic generation of classifiers and Big Data semantic processing.

Both Retina API and Retina Library are provided with a default database for general English which can be replaced with any other language or domain-specific semantic space.

Retina API and Retina Library serve as a central resource for a multitude of different applications that elegantly solve difficult NLU tasks where other approaches struggle.

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Proud to be named an IDC Innovator for the 2016 Machine Learning-Based Text Analytics Market

03 Jun 2016

The latest IDC Innovator research report, IDC Innovators: Machine Learning-Based Text Analytics, 2016 (Doc # US41312116, May 2016) recognizes Cortical.io as an IDC Innovator.

Cortical.io’s approach to understanding language, Semantic Folding, is inspired by the latest findings on the way the brain processes information. It proposes a statistics-free processing model based on semantic fingerprints, a new data representation that encodes meaning explicitly, including all senses and contexts. Cortical.io’s Retina engine learns any language by ingesting relevant text content via unsupervised learning.

Cortical.io’s Retina engine reduces complex text analytics to the application of a simple similarity function. This makes the system both highly scalable and very intuitive to use. With 16,000 semantic features, the Retina engine performs fine-grained deep semantics on any unstructured text and is completely language independent. Because it is highly efficient, the Retina Engine reduces drastically the computing power needed to process terabytes of data. It enables high performance semantic processing applications like on-the-fly classification, streaming text filtering or semantic search by analogy and opens up a new range of applications for social media monitoring, enterprise search, forensic text analytics, information discovery, compliance monitoring and much more.

IDC Innovators reports present a set of vendors chosen by an IDC analyst within a specific market that offer an innovative new technology, a groundbreaking approach to an existing issue, and/or an interesting new business model. It is not an exhaustive evaluation of all companies in a segment or a comparative ranking of the companies. IDC INNOVATOR and IDC INNOVATORS are trademarks of International Data Group, Inc.

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Semantic Folding helps improve prediction of stock return correlations

13 Apr 2016

A recent academic study conducted by researchers from Leiden, Ben-Gurion and Toulouse Universities examined the performance of Cortical.io’s Semantic Folding approach for content analysis in a finance setting. Compared to the commonly used word-list method, Semantic Folding proved to have greater predictive power. Its other advantages were speed and ease of use.

“Like the human brain, our Semantic Folding engine learns a language and understands the meaning of text by making analogies. Like the brain, it is both efficient and accurate. We are thrilled to see these compelling results confirmed by an independent academic study”, comments Francisco Webber, inventor and co-founder of Cortical.io.

The research team used Cortical.io’s Retina API to create semantic fingerprints of the 30 Dow Jones Industrial Average constituents, based on business description sections of the companies’ annual reports. For each pair of companies, the similarity of their semantic fingerprints was compared to predict correlations between their stock returns over the following year.

The study found Semantic Folding to have greater predictive power than the traditional word-list based approach. Moreover, fingerprint similarity continued to significantly predict stock return correlations even when other measures of company similarity were controlled for.

The authors contend that Semantic Folding is simpler to use, has lower setup costs, and runs faster than the standard word-list based method. In addition, semantic fingerprints were considered to have an appealing visual interpretation. The authors argue that Semantic Folding significantly lowers the entry barriers for investigators interested in applying content analysis to financial data. To this end, the study includes sample code and suggests possible applications of Cortical.io’s Semantic Folding engine in several finance contexts.

The study, entitled “Using Semantic Fingerprinting in Finance” is available here.

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Presenting Retina Spark 2.0

03 Mar 2016

Cortical.io presents Retina Spark 2.0, an NLP tool specially designed for high performance semantic text processing in an Apache Spark environment. Similar to the Retina API, it operates on the semantic rather than the keyword level and measures the similarity in meaning between text passages in order to classify, filter and search large document repositories.

Retina Spark 2.0 enables the creation of:

  • an index of text or document semantic fingerprints to efficiently search terabytes of unstructured text data
  • a semantic classifier based on positive examples of a class
  • a semantic filter for high-throughput text streams (e.g. Twitter feed)

Retina Spark 2.0 is a library that augments Spark MLlib with high-performance semantic text processing capabilities. It is Cloudera certified and can be used with on-premise or in-the-cloud Spark clusters, including those based on the Cloudera and Amazon EMR distributions. Retina Spark 2.0 supports the latest Apache Spark releases and features a Java and Scala API.

Apache Spark is an open source framework and runtime environment for distributed and parallel computing.


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Proudly Presenting our White Paper about Semantic Folding Theory

25 Nov 2015


It is common scientific practice to investigate phenomena, which cannot be explained by an existing set of theories, scientifically by applying statistical methods. This is how medical research has led to coherent treatment procedures, which provided a great deal of usefulness to patients. By observing many cases of a disease and by identifying and accounting its various cause and effect relationships, the statistical evaluation of these records allowed to make thoughtful predictions and to find adequate treatments as countermeasures. Nevertheless, since the rise of molecular biology and genetics, we can observe how medical science moves from the time-consuming trial and error strategy to a much more efficient, deterministic procedure that is grounded on solid theories and will eventually lead to a fully personalized medicine.

The science of language had a very similar development. In the beginning, extensive statistics analyses led to a good analytical understanding of the nature and the functioning of human language and culminated in the discipline of linguistics. With the increasing involvement of computer science into the field of linguistics, it turned out that the observed linguistic rules were extremely hard to use for the computational interpretation of language. In order to allow computer systems to perform language based tasks comparable to humans, a computational theory of language was needed and as no such theory was available, research turned again towards a statistical approach by creating various computational language models derived from simple word count statistics. Although there were initial successes, statistical Natural Language Processing (NLP) suffers two main flaws: The achievable precision is always lower than the one of humans and the algorithmic frameworks are chronically inefficient.

The Semantic Folding Theory (SFT) is the attempt to develop an alternative computational theory for the processing of language data. While nearly all current methods of processing natural language based on its meaning use in some form or other word statistics, Semantic Folding uses a neuroscience rooted mechanism of distributional semantics. After capturing a given semantic universe of a reference set of documents by means of a fully unsupervised mechanism, the resulting semantic space is folded into each and every word-representation vector. These vectors are large, sparsely filled binary vectors. Every feature bit in this vector not only corresponds but also equals a specific semantic feature of the folded-in semantic space and is therefore semantically grounded. The resulting word-vectors are fully conforming to the requirements for valid word- SDRs (Sparse Distributed Representation) in the context of the Hierarchical Temporal Memory (HTM) theory by Jeff Hawkins. While the HTM theory focuses on the cortical mechanism for identifying, memorizing and predicting reoccurring sequences of SDR patterns, the Semantic Folding theory describes the encoding mechanism that converts semantic input data into a valid SDR format, directly usable by HTM networks.

The main advantage of using the SDR format is that it allows any data-items to be directly compared. In fact, it turns out that by applying Boolean operators and a similarity function, many Natural Language Processing operations can be implemented in a very elegant and efficient way.

Douglas R. Hofstadter’s Analogy as the Core of Cognition is a rich source for theoretical background on mental computation by analogy. In order to allow the brain to make sense of the world by identifying and applying analogies, all input data must be presented to the neo-cortex as a representation that is suited for the application of a distance measure.

The two faculties - making analogies and making predictions based on previous experiences - seem to be essential and could even be sufficient for the emergence of human-like intelligence.

Download full White Paper

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USD 1.8 million for brain-inspired algorithm made in Austria

29 Oct 2015

Cortical.io, an innovator in natural language processing (NLP), announces its next venture capital round. In this third round, Cortical.io opens its capital to a new investor from the US, a fund affiliated with Open Field Capital (OFC), an investment manager with a focus on emerging technology markets. After Numenta, OFC is the second US-based investor taking an ownership position in Cortical.io. Reventon (NL) confirms its interest in the machine intelligence start-up with an additional participation, bringing the capital increase to a total of USD 1.8 million.

Cortical.io’s approach of using similarity as a foundation for intelligence should enable a NLP technology that not only outperforms legacy systems in traditional text processing, but also opens a new range of applications that were not possible before, because it has the potential to eliminate constraints related to processing speed, data volume and the diversity of natural languages. This is exactly the kind of market disruption potential that we seek in an investment”, describes Marc Weiss, Principal at OFC.

Together with the third capital round, Cortical.io announces the opening of an office in the San Francisco Bay Area, where its sales and business development activities will be based. “North America is a core market for intelligent text analytics”, explains Francisco Webber, CEO and co-founder of Cortical.io. “There is a lot of value still hidden in Big Text Data. While our technology can be applied to many different business cases, its algorithmic efficiency has triggered strong interest from the financial industry. In the context of compliance monitoring, for example, the high precision and recall scores help to substantially reduce the associated workload”, explains Webber before concluding: “Our solution could help banks save billions in legal bills.”

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Numenta and Cortical.io Form Strategic Partnership

14 May 2015

Numenta, Inc., a leader in machine intelligence, and Cortical.io, an innovator in natural language processing (NLP), are pleased to announce a strategic partnership to create a new computing approach to understanding text. As part of the strategic relationship, Cortical.io has taken a broad general license to Numenta’s Hierarchical Temporal Memory (HTM) technology, and Numenta has taken an ownership position in Cortical.io. The combination of Cortical.io’s Semantic Folding technology and Numenta’s HTM technology enables a host of exciting applications that have challenged computer scientists for decades, including sentiment analysis, automatic summarization, semantic search, and conversational dialogue systems.

“Cortical.io’s Semantic Folding technology is a clever and elegant way to feed natural language into our HTM technology”, said Jeff Hawkins, founder of Numenta. “Cortical.io takes advantage of the semantic encoding and predictive modeling of HTM systems in a way that will lead to significant advances in natural language processing.”

“Natural language understanding is one of the central problems of artificial intelligence,” said Francisco Webber, founder and CEO of Cortical.io. “We aim to build the next generation of NLP, Language Intelligence, and in so doing, show the path to broadly applied machine intelligence.”

Building on their existing commercial product, the Retina API, Cortical.io will make the combined technologies available through their industrial-grade cloud service for customers ranging from innovative startups to international corporations.

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Cortical.io secures $ 1.25M in additional venture capital

06 Nov 2014

The Austrian science start-up Cortical.io has just secured a next venture round of 1.25 million dollar of growth capital from Reventon (NL). This will help to bring Cortical.io’s portfolio of language intelligence products to the global app-builder and enterprise market.

Based on the breakthrough neuroscience theory of Jeff Hawkins, Cortical.io’s semantic fingerprinting technology represents language like in the human brain.

Cortical.io’s Retina API allows to create semantic fingerprints of any piece of text in any language. Fingerprints of product descriptions in English can be compared to LinkedIn profiles in German, documents in Spanish compared to reading preferences of French readers, multi-language twitter messages filtered by their content and job profiles related to CVs.

With Cortical.io’s Retina everything that can be described in words can be intelligently matched based on its meaning.

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Proudly presenting Cortical.io, a service for Language Intelligence

01 Jun 2014

We have developed a technology that enables developers to perform natural language processing in an intuitive and precise manner. “Our Semantic Fingerprinting method enables the creation of a unique semantic fingerprint for any word, any document, and in the near future even for any entity that can be described with natural language”, explains Francisco Webber, co-founder of Cortical.io. The big difference to conventional semantic systems is that the conversion of words
into their semantic fingerprints is automated. There is no need for costly, time-consuming manual intervention anymore.

The core component of Cortical.io, the Retina, learns about the essence of any language by reading text material about the world and is capable of semantically interpreting and computing any textual content. It encodes words in the same way as information is fed into the brain and generates semantic fingerprints of words and documents using a fine-grained representation of 16,000 semantic features for every term.

The invention of Cortical.io’s Retina could revolutionize the search and analysis of text-based information, not only because of its transparency and simplicity of use, but also because of its small footprint: huge amounts of text -structured and unstructured- can be processed with moderate computational power.

By converting any piece of text into a semantic fingerprint, tasks such as similarity comparison, contextual keyword generation, sense disambiguation, and document classification are made simple. Cortical.io’s Semantic Fingerprinting method can be applied to messages, news, web content, document collections and even real-time text streams from social networks.

The Cortical.io service is accessible through a REST API. Currently, we offer SDKs for Java and Javascript. SDKs for python, .net and other development platforms will be available soon.

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Choose the best out of two Retinas for your application.

01 May 2014

With the new API release, you can easily select different Retinas and get the most adequate results, whether you want to focus on context-similarity or synonym-similarity. If your goal is to disambiguate terms, i.e. identify which meanings are contained within a specific term, you will want to use an associatively focused Retina (“en_associative” in our API). If you prefer identifying synonymous items for terms or texts, then a synonymously focused Retina (“en_synonymous” in our API) will deliver better results.

More details about the two retinas are available in our FAQs.

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