Cognitive computing tools

List of Top 7 Cognitive Computing Tools

With a lot of advantages, Cognitive Computing still has some disadvantages like limited analysis of risks as well

To make any computer model mimic the action of the human brain would be the next technological marvel.

Cognitive computing works on the principle of artificial intelligence which aims at imitating human thoughts within a computerized model.

Such systems have the efficiency of continuously grasping knowledge from the sorted data that is fed to them.

A lot of data is fed regularly to such systems. This aims to improvise such systems in terms of decision making as well as predicting certain patterns.

Some of the Best Cognitive Computing Tools


Failure of applying good maintenance can surely disrupt the whole chain of industrial operations.

To overcome this paradigm of maintenance Spark Cognition’s analytical solution SparkPredict was introduced.

It helped in overcoming the maintenance downtime and thus boosting the overall operational costs savings.

SparkPredict analyzes various data whether structured or unstructured. It then uses machine learning techniques to revert with appropriate actions acceptable at that time.

Machine learning techniques help this tool to become efficient enough in predetermining errors or patterns.

TCS ignio™ Cheetah

Launched in the year 2015, ignio was introduced with the aim of combining three main pillars of technology. These were machine learning, artificial intelligence and advanced software engineering.

The main aim of this platform was to autonomously curb the issues whenever they arise. It is a cognitive automation software package specializing in accelerating deployments and value realization by customers

ignio™ Cheetah has added new features to its earlier properties of blueprinting as well as automation facilities like:

  • Management of priority events.
  • Predicting the tasks that are in urgent need of attention.
  • Reduction of false alerts.
  • Efficient enough to handle the incidents with understanding from its past history.

By adaptive property not only it understands real-time human actions but also becomes adaptive to the technologies around.

This property of ignio™ cheetah making it easier for many of its users to be able to extend the functionality of supporting new technologiesies.

Some of the features of ignio™ are:

  • Support to cloud deployments.
  • SaaS engagement models.
  • Ability to handle larger data volumes.

Iris by Apixio

Data accessibility still remains to be a big challenge for technology giants. Majority of the issues are repeatedly found in the healthcare sector.

With federal agencies getting strict on healthcare organizations are asked for providing better data through robust measurements.

Thus, there was a need for niche product to deliver the same.

Apixio Inc., an AI healthcare analytics company is bridging this gap with the introduction of its cognitive computing platform Iris.

Iris uses doctor’s notes and records to derive insights from the same.

Iris uses machine learning model which is fed with the data that has been extracted through data integration tools as well as real-time data providing tools.

AlphaGo by Google

AlphaGo was introduced as a cognitive computing tool in the field of playing the board games.

It works on the algorithms utilizing a combination of techniques like machine learning, tree traversal, and deep neural network technology.

Before the input is sent to the neural networks, a bit of game supportive feature is applied to it.

Both humans and computers are given extensive training for this cognitive computing tool.

In the initial stages, the neural networks were made to analyze the human gameplay behavior. To make the AlphaGo intelligent enough to beat the board grandmasters, it was also made to mimic the moves from the historic recorded games.

Also Read: Real-Life Examples of Cognitive Computing

Aila by Enterra Solutions

Aila was introduced by Enterra Solutions. It aims to tackle questions that are unanswerable by traditional data analytics.

It aims for minimum dependency on the data or data experts and generating cost-effective insights.

Aila combines both artificial intelligence and advanced mathematics to solve problems of humans. Unlike earlier enterprise software that had their tasks predefined, Aila is something different when it comes to working.

Aila is a cognitive system that improvises itself on the basis of experience and generates insights on demand.

Aila, unlike other tools, works on the logic to differentiate complex interactions among variables.

It taps into the storehouses of information with knowledge about your business. It also bypasses the statistical barriers to derive the insights and that too quickly and on-demand.

Cortex Certifai by CognitiveScale

It’s basically an auditing tool for AI. It was introduced with the sole aim of bridging the trust gap between AI and its deliverance.

Cortex Certifai does so by using AI for the detection of vulnerabilities in black boxes without being dependent on their access.

AI is smart enough to improve itself and has a tendency to outsmart humans. This remains a reason that before AI to be adopted and to trust it, there needs to be confident in its decisions.

It has been awarded Global Annual Achievement Awards for Artificial Intelligence for responsible AI and ethics.

CognitiveScale is currently working on the counterfactual fingerprinting technology. Also on Cortex Certifai product with advanced features including multi-user role-based dashboard.

IBM Watson

IBM Watson mainly focusses on healthcare data.

It was introduced to support healthcare in curbing the frauds and provide better medical care. And also to help in strategic decisions related to treatments.

It derives its findings from the medical literature for its correlation with symptoms. IBM Watson shifts through data libraries to discover the insights for answering questions

Like other tools, it also gets smarter with every interaction and findings. Thus it is able to bring back relevant answers for certain questions.

It does not work on any predefined rule, instead, it generates hypotheses based on the vast amount of data and potential connections.

IBM Watson Explorer Engine is one of the offerings from IBM Watson. It has the capability of converting unstructured data to derive information from it.

It works in three stages:

  • Data feeding
  • Data indexing
  • Linguistic processing

When the data is fed, it gets indexed which ultimately helps the developers in determining its cognitive features.

After this, Watson performs position-based indexing.

This type of indexing has added advantages over the traditional one which is almost used universally in open source search systems.

IBM Watson’s use of applying positional indexing helps the data to liberate itself from the traditional rigid document models

The linguistic process is done while data is ready for indexing and again done whenever there is a query from the user.

Another one amongst many important features of IBM Watson is that of alerts, which it provides via mails.

Cognitive computing tools with their intelligentsia have been helpful at drawing various strategic conclusions. Our above-mentioned list of tools is some of the best available in the market for you to choose from.

Also Read: 

Understanding Neural Network Architectures for Machine Learning

Benefits of Machine Learning in ERP

About Jason Hoffman

I am the Director of Sales and Marketing at Wisdomplexus, capturing market share with E-mail marketing, Blogs and Social media promotion. I spend major part of my day geeking out on all the latest technology trends like artificial intelligence, machine learning, deep learning, cloud computing, 5G and many more. You can read my opinion in regards to these technologies via blogs on our website.