AI/Machine Learning/NLP/AR/VR 2018-03-13T16:28:30+00:00
Quantilus AI NLP ML

Artificial Intelligence, Machine Learning, Natural Language Processing, Augmented and Virtual Reality

Intelligent Automation for Work and Life

At Quantilus, we have been working with AI before it became cool (and scary). Our first foray was in the field of Natural Language Processing – which we used for automated grammar and style checks of written content. Subsequently, we built tools to classify untagged content in intelligent, usable ways, and to present it for consumption with a high degree of personalization. More recently, we have been working on personality assessment of individuals based on 1) the words they speak (a relatively simple task), and 2) changes in their facial patterns based on verbal and visual cues (a much more complex task).

Want to build Virtual Reality or Augmented Reality apps for your business? We built some of the first business-focused AR apps for mobile and wearable platforms through our SAP partnership. Our apps help technical support personnel visualize product models, and also let customers visualize retail products in empty space. With the added complexity of tight integration with backend ERP systems.


Appliqant - Automated Video Interviews
APPLIQANT – the Automated Interview Robot. Disrupt recruitment through the automated screening of job candidates.
Appliqant - Automated Video Interviews
Wearable Apps for Technical Support

Discover Simple Assist – Wearable Apps for Technical Support

Wearable Apps for Technical Support
Visual Showroom - Augmented Reality

Visual Showroom – Augmented Reality App for Product Display

Visual Showroom - Augmented Reality
Deloitte - Automated Document Editing, Data Extraction

Deloitte – Automated Document Editing, Validation and Data Extraction

Deloitte - Automated Document Editing, Data Extraction
Intel - Machine Learning

Intel – Machine Learning for Customer Classification and Segmentation

Intel - Machine Learning
Intelligent Lease - Data Extraction using NLP

Intelligent Lease – Automated Data Extraction from Unstructured Lease Documents

Intelligent Lease - Data Extraction using NLP
BluePencil - Grammar and Writing Style Checks using NLP
BluePencil – Automated Grammar and Writing Style Checking using NLP
BluePencil - Grammar and Writing Style Checks using NLP


Some of the frameworks and tools that our development teams have used recently. A list that grows by the day.


Relevant, interesting and current curated research content in the field.

Why is Machine Learning Important and How will it Impact Business?

July 31st, 2020|Categories: AI, NLP, Machine Learning|Tags: , |

It’s a bit difficult to narrow down one specific definition of machine learning (ML) because you’ll get a different explanation depending on whom you ask.

Nvidia defines machine learning as “the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” McKinsey&Company agree with Nvidia, saying that machine learning is “based on algorithms that can learn from data without relying on rules-based programming.” Stanford suggests that machine learning is “the science of getting computers to act without being explicitly programmed.”

And Carnegie Mellon’s definition—also a favorite of many other experts in the machine learning industry—states “the field of Machine Learning seeks to answer the question ‘How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?’”

Regardless of the definition you choose, at its most basic level, the goal of machine learning is to adapt to new data independently and make decisions and recommendations based on thousands of calculations and analyses. It’s done by infusing artificial intelligence machines or deep learning business applications from the data they’re fed. Machine learning models learn, identify patterns, and make decisions with minimal intervention from humans. Ideally, machines increase accuracy and efficiency and remove (or greatly reduce) the possibility of human error.

The importance of machine learning

The nearly limitless quantity of available data, affordable data storage, and the growth of less expensive and more powerful processing has propelled the growth of machine learning. Now many industries are developing more robust machine learning models capable of analyzing bigger and more complex data while delivering faster, more accurate results on vast scales. Machine learning tools enable organizations to more quickly identify profitable opportunities and potential risks.

The practical applications of machine learning drive business results which can dramatically affect a company’s bottom line. New techniques in the field are evolving rapidly and expanded the application of machine learning to nearly limitless possibilities. Industries that depend on vast quantities of data—and need a system to analyze it efficiently and accurately, have embraced machine learning as the best way to build models, strategize, and plan.

Industries that use machine learning

Healthcare. The proliferation of wearable sensors and devices that monitor everything from pulse rates and steps walked to oxygen and sugar levels and even sleeping patterns have generated a significant volume of data that enables doctors to assess their patients’ health in real-time. One new machine learning algorithm detects cancerous tumors on mammograms; another identifies skin cancer; a third can analyze retinal images to diagnose diabetic retinopathy.

Government. Systems that use machine learning enable government officials to use data to predict potential future scenarios and adapt to rapidly changing situations. Machine learning can help to improve cybersecurity and cyber intelligence, support counterterrorism efforts, optimize operational preparedness, logistics management, and predictive maintenance, and reduce failure rates. This recent article highlights 10 more applications for machine learning within the healthcare industry.

Marketing and sales. Machine learning is even revolutionizing the marketing sector as many companies have successfully implemented artificial intelligence (AI) and machine learning to increase and enhance customer satisfaction by over 10%. In fact, according to Forbes, “57% of enterprise executives believe that the most important growth benefit of AI and machine learning will be improving customer experiences and support.

E-commerce and social media sites use machine learning to analyze your buying and search history—and make recommendations on other items to purchase, based on your past habits. Many experts theorize that the future of retail will be driven by AI and machine learning as deep learning business applications become even more adept at capturing, analyzing, and using data to personalize individuals’ shopping experiences and develop customized targeted marketing campaigns.

Transportation. Efficiency and accuracy are key to profitability within this sector; so is the ability to predict and mitigate potential problems. Machine learning’s data analysis and modeling functions dovetail perfectly with businesses within the delivery, public transportation, and freight transport sectors. Machine learning uses algorithms to find factors that positively and negatively impact a supply chain’s success, making machine learning a critical component within supply chain management.

Within logistics, machine learning facilitates the ability of schedulers to optimize carrier selection, rating, routing, and QC processes, which saves money and improves efficiency. Machine learning’s ability to analyze thousands of data points simultaneously and apply algorithms more quickly than any human enables machine learning to solve problems that people haven’t yet identified.

Financial services. The insights provided by machine learning in this industry allow investors to identify new opportunities or know when to trade. Data mining pinpoints high-risk clients and informs cyber surveillance to find and mitigate signs of fraud. Machine learning can help calibrate financial portfolios or assess risk for loans and insurance underwriting.

The future of AI and machine learning in this industry include an ability to evaluate hedge funds and analyze stock market movement to make financial recommendations. Machine learning may render usernames, passwords, and security questions obsolete by taking anomaly -detection to the next level: facial or voice recognition, or other biometric data.

Oil and gas. Machine learning and AI are already working to find new energy sources and analyze mineral deposits in the ground, predict refinery sensor failure, and streamline oil distribution to increase efficiency and shrink costs. Machine learning is revolutionizing the industry with its case-based reasoning, reservoir modeling, and drill floor automation, too. And above all, machine learning is helping to make this dangerous industry safer.

Manufacturing. Machine learning is no stranger to the vast manufacturing industry, either. Machine learning applications in manufacturing are about accomplishing the goal of improving operations from conceptualization to final delivery, significantly reducing error rates, improving predictive maintenance, and increasing inventory turn.

Not unlike the transportation industry, machine learning has helped companies improve logistical solutions that include assets, supply chain, and inventory management. Machine learning also plays a key role in enhancing overall equipment effectiveness (OEE) by measuring the availability, performance, and quality of assembly equipment.

Machine learning & artificial intelligence: here to stay

Is all the hype surrounding machine learning really worth it? Most experts say “yes” – with this caveat: The key is understanding how to use it to meet each individual business’s challenges and goals. It’s clear, based on a significant volume of data and evidence, that machine learning and artificial intelligence are here to stay. The trick, however, is recognizing that machine learning and AI aren’t a magic spell that works for every situation.

Experts agree that it’s important to clearly understand the value that incorporating machine learning will bring to your business. If it’s negligible, the expense may not bring a significant enough return on investment (ROI). This article from Business highlights four questions to ask before you consider beginning a machine learning project.

Why is machine learning important for your business in particular? Contact us at for a consultation and learn more about what Quantilus has to offer here.

5 Ways Your Company Can Benefit From Robotic Process Automation

July 30th, 2020|Categories: AI, NLP, Machine Learning|Tags: |

5 Ways Your Company Can Benefit From Robotic Process Automation 

In almost every business, mundane tasks exist. Tasks that employees wish someone else or something else would complete for them so they can focus on more pressing assignments. This is where robotic process automation enters the equation.

What is Robotic Process Automation?

What exactly is robotic process automation (RPA)? Contrary to popular belief, there is no physical, moving robot in robotic process automation. Instead, the robot or ‘bot’ is software implemented into a machine, which in most cases is a computer. This bot can then be trained by a human to complete a variety of repetitive computer tasks with little to zero errors. 

As a side note, RPA should not be confused with artificial intelligence. To differentiate the two, robotic process automation can be associated with “doing”, while artificial intelligence aligns with “learning” and “thinking”.  

Certain qualifications should be met to ensure a task is worth incorporating robotic process automation. David Landreman, the CPO at Olive which is an artificial intelligence company, lists them as

  1. The process must be rule-based.
  2. The process must be repeated at regular intervals, or have a pre-defined trigger.
  3. The process must have defined inputs and outputs.
  4. The task should have sufficient volume.

Assuming the task aligns with these four qualifications, businesses should reap the many benefits of integrating robotic process automation.


Use Cases for Robotic Process Automation

Before diving into five use cases of robotic process automation, let’s go over a real-world example of how RPA is used. There are boundless use cases for robotic process automation, including implementing RPA for collecting data, handling transactions, and capturing information. One of the most commonly used applications of RPA is data migration and form processing. For example, if a business is transitioning from paper forms to a digital database, RPA can analyze the paper forms, extract the required data, and then enter the collected data into the system. Therefore, RPA almost entirely removes the human from this tedious and time-consuming process.


The Benefits

Now that we covered what robotic process automation is and some examples of how it can be used, let’s reveal some of the numerous benefits RPA offers.

  • Greater Efficiency
    • RPA is efficient. Once the robot is trained with a specific set of instructions, it can complete a task much quicker than its human counterpart. The robot does not require breaks, does not experience distractions, and can work around the clock.
  • Increased Productivity
    • Implementing RPA allows human employees to focus their attention on more pressing tasks. No longer do employees need to waste time on repetitive, manual tasks that are better suited for robotic process automation. Instead they can use their time tackling complex assignments that a robot is not able to solve.
  • Cost Savings
    • RPA is a great way to help a company’s bottom line. Less full-time workers are needed because employees are not spending time completing the mundane tasks which robotic process automation can handle. David Schatsky, a managing director at Deloitte LP, reveals the savings one bank experienced while utilizing RPA, “Deploying 85 bots to run 13 processes, handling 1.5 million requests per year. The bank added capacity equivalent to more than 200 full-time employees at approximately 30 percent of the cost of recruiting more staff.”
  • Unmatched Accuracy
    • Humans are not perfect, and consequently, make mistakes. Whether it is attaching the wrong file to an email or spelling the address wrong on a tax form, errors occur. However, RPA rarely, if ever, makes mistakes since it is specially trained software.
  • Unlimited Scalability
    • There are three main ways to RPA automation to fit the growing needs of a business:
      1. Increasing the bots workload. This simply means providing the bots with a greater share of assignments.
      2. Diversifying the bots’ responsibilities. Bots can understand and complete different processes. Thus, when a bot is caught up on work in one process, it can switch gears to another different process.
      3. Expanding RPA access. With new technologies continually being created, robotic process automation is regularly updated with new capabilities. By integrating new software to a company’s robotic process automation solutions, RPAs can be scaled by increasing their workloads and diversifying their responsibilities.


Implementing Robotic Process Automation 

If utilized correctly, RPA saves businesses and employees time, money, and accuracy. Three things everyone likes to hear. Whether it’s a bank processing credit and background checks, or a health insurance company processing claims, robotic process automation’s versatility can be deployed. For more information on robotic process automation, please reach out to us at

5 Ways to Incorporate Artificial Intelligence Into Your Small or Medium-Sized Business

July 17th, 2020|Categories: AI, NLP, Machine Learning|Tags: , , , , , , , , , , , |

In the entertainment industry, artificial intelligence or AI is commonly associated with sci-fi movies featuring human-like robots that are bent on world domination. On the other hand, news articles often link artificial intelligence to replacing human jobs with all-knowing machines. Both of these are misconstrued concepts that give artificial intelligence a bad rap.

In reality, artificial intelligence is an affordable and accessible resource that companies are becoming increasingly reliant on to solve various challenges. Let’s dive into five ways to incorporate artificial intelligence into a small or medium-sized business.



With more employees working from home, cybersecurity is more important than ever. Most employees are not working on their company’s secured wifi, which assists in protecting confidential information. Working on a less secure network leaves computers and their data vulnerable to hackers. For example, hackers use armies of internet bots that continuously work to poke holes into systems and access restricted data. Once these bots infiltrate the servers, they install a lock. Companies usually have to send the hackers money to regain access to their data.

Artificial intelligence can protect your network by creating bots to scope out weaknesses in your network. Once a vulnerability is detected, they will flag them before you do get hacked. So instead of being reactive, artificial intelligence allows companies to be proactive in defending themselves and their employees.


Hiring Employees

Hiring employees can be a lengthy and stressful process. Not only does it take a great deal of time and money to research and interview candidates, but if a new hire is not a good match, it can be expensive to rehire and train a new employee.

This is where artificial intelligence enters the equation. Artificial intelligence can pull together the best candidates from various sources, such as thousands of online job boards. Once the best candidates are filtered in, artificial intelligence can narrow down candidates using a variety of different methods. For example, software such as Appliqant utilizes automated video interviews of candidates, comparing personality profiles to that of a company, analyzing resumes, examining skills assessments, and more. After a full assessment is compiled and artificial intelligence identifies the top selections, companies are only left with interviewing candidates and picking the best person for the job. Artificial intelligence provides hiring managers more time to focus on day-to-day tasks instead of the recruiting and hiring process.


Targeting Sales and Marketing

Marketing is an essential aspect of any business, and there are countless different marketing tactics from which to choose. Whether it is television ads, digital marketing campaigns, social media, or public relations. With so many options available, it can be challenging to determine the best choices and how to allocate valuable resources properly.

Artificial intelligence benefits marketing teams because it can analyze data that might otherwise go undiscovered. Artificial intelligence can collect and study data on customers that enter a physical store, visit an e-commerce site, or have an online presence (such as social media). By analyzing customer data and behaviors, AI helps marketers identify and target the customers most likely to convert into buyers. AI also determines where the marketing dollars have the greatest return on investment, which takes the guesswork out of marketing.

It is important to note that this technology does not have to be created from scratch. Tools already exist in the marketplace, such as Conversica, Gong, Node, ScaleX, and Cortex. If leveraged correctly, these tools can be combined to build a personal and holistic analytical solution for any company. 


Customer Service

One of the most popular ways to incorporate artificial intelligence into customer service is using text chatbots. When chatbots first debuted, they would commonly respond to consumers with fixed responses and were tasked with connecting consumers to the right customer service representative. For example, consumers would go to a company’s website and begin a conversation with a bot. If the consumer told the bot they wanted to talk with someone, the bot would respond with fixed answers such as…

 “Would you like to make a return?”

 “Do you want to place an order?”

 “Do you want to call customer service?”

Depending on the consumer’s answer, the bot would transfer the consumer to the appropriate department where a human employee would begin to solve the issue.

However, the chatbot’s goal now is to see how far it can go without having a human employee intervene. Therefore, bots can now respond to unstructured questions that are more advanced than basic inquiries. All of which can be accomplished without needing human assistance to complete consumer requests. This allows human customer service agents to focus their time and efforts on the most complex cases. Lastly, chatbots enable companies to answer consumers 24/7, thus improving response time, which enhances the customer experience.


Inventory and Delivery Management 

Predictive analytics is made possible by artificial intelligence, and they are vital to inventory and delivery management. Predictive analytics can identify trends and patterns in the market by looking at data (the more data, the better) to predict which products will be needed over a period of time. This allows companies to manage orders and inventory more accurately, thus ensuring all future requests can be fulfilled without possessing excess stock.

There is also predictive maintenance when artificial intelligence predicts when a machine will need maintenance and identifies the optimal cycle for maintaining machines. Therefore, the likelihood of a machine breaking and consequently delaying production or shipping is significantly reduced. Lastly, artificial intelligence helps with quality checks. Machines can be trained to scan products and identify imperfections before leaving the factory, therefore reducing human error in spotting faulty products.


The Value of AI

Artificial intelligence is an extremely versatile tool that is implemented to solve a myriad of issues and make processes more efficient. It can save your business time and money while simultaneously maximizing revenue opportunities. Whether you want to improve targeting sales and marketing tactics, customer service, the hiring process, inventory and delivery management, cybersecurity, or another aspect of your business, there is a substantial chance artificial intelligence can help. Artificial intelligence is vital for the future of business, so implementing it in various capacities will help your company stay ahead of the curve.

Apple WWDC 2020: One Seamless Ecosystem

July 1st, 2020|Categories: AI, NLP, Machine Learning, Mobile Development, News and Announcements|

Image credits: Apple 

The announcements from this year’s Apple Worldwide Developers Conference can be summed in three wordsefficiencyconsistency, and control. As software and app developers, Quantilus is keenly interested in Apple’s vision as it determines what we are able to create and build for clients on Macs, iPhones, and iPads. New features and capabilities mean new possibilities. The news with the most impact relates to the Mac but has broader implications across iPhone and iPad in the future.  

Custom-built Apple CPU: A Monumental Shift 

The significant announcement—and certainly the one with the most buzz—is Apple’s plan to transition Mac products from Intel processors to its homegrown ARM-based chips called Apple SiliconThe new silicon chips are similar to the processors Apple uses for iPhones and iPads. Apple plans to release the first siliconpowered Mac by the end of 2020, with the full transition of the entire Mac product line occurring over the next two years. But not to worry, Apple will continue to support and release new Intel-powered Macs for years to come, so it’s not exclusive (at least for now). It does send a clear message that Apply will not rely on Intel moving forward. The move will end Apple’s dependence on a third-party CPU, which tended to dictate what, when, and how of its product lineup. Apple will now have complete control and will reap efficiencies with the integration of hardware, software, and services across its entire family of products. As a result, Apple can build better products across the board.  

So, what does this all mean for developers, clients, and end-users? The fact that iOS and iPadOS apps can run natively on silicon-powered Macs is a key benefit because an app can now work across all devices. One common and scalable architecture across all the Apple devices makes it easier for developers to create software across Mac laptops/desktops (MacOS), iPhones (iOS), and iPads (iPadOS), which equates to reduced time and costs. Additionally, the new processors are faster and offer higher performance at lower power consumption, which means better processing, improved graphics performance, and longer battery life—all valuable features for end-users.  

MacOS Big Sur: Bridging the Gap 

Touted by Apple as the biggest design update since MacOS X, the release of Big Sur (MacOS 11) offers refreshed designs for all apps on the system, including calendar, notes, podcasts, and music. The new operating system also boasts a revamped Safari browser with improved tab management capabilities, new privacy features, browser extensions, and built-in language translationOther notables include updates to Messages that offer better conversation management and group messaging options and a redesigned Maps app featuring Guides from trusted sources and 360-degree views of destinations.  

More so than the new visual look, Big Sur represents Apple’s vision of a consistent experience across its family of productsIt is a look that Apple describes as “entirely new but instantly familiar.” While the modern user interface and user experience are new to the Mac, one can’t help to notice that it looks like iOS on a computerAdditionally, the unique experience of Big Sur includes some features, such as widgets, messages, and guides, touted in the next release in the fall for iOS and iPadOSThe underlying theme is a cohesive experience across devices for Apple customersThe average American household owns 2.6 Apple products, and that number jumps to 4.7 products for affluent households. Consequently, the familiarity here is a positive takeaway because of the universal apps and features means that the user experience is consistent. Users will not need to learn how to do the same task different way on a different device.     

iOS14 and iPadOS 14: Improved usability and access 

The news for iPhones and iPads focused on updates to system software. Apple’s next iOS and iPadOS release in the fall will offer users new and improved usability, features, and experiences with their phones and tablets. All great news for the 193 million iPhone users in the United States.  

Apple has wholly rethought apps management and organization on the iPhone. Common user frustrations are addressed, and app content will be served and customized to the user thanks to on-device machine learning. Consequently, developers, clients, and end-users will have better access and visibility for installed and/or frequently used apps.  

The new capabilities to look forward to with iOS14 include: 

  • WidgetsWidgets—offering timely content at a glance—can be added to the home screen based on your interests. Additionally, smartstack will surface the widget you’ll want to use next based on the user’s behavioral patterns of activity, time, and location. 
  • App Library: The new App Library will put an end to the endless sifting and search for apps across multiple screens. App Library will organize all the apps into smart folders  and in an easier-to-navigate view. And, like the widgets, the App Library will organize and predictively serve up the apps when you need it. 
  • Compact Call User Interface: No more full-screen takeovers while on a call or using Siri.  A new compact design for these interactions allows simultaneous access and use of your screen Additionally, picture-in-picture comes to the iPhone, allowing for multitasking while on a Facetime call or watching a video. Users now will be able to Facetime chat with friend about where to go for dinner while searching for restaurants on Google 
  • App Clips: Looking to pay for your parking or coffee with an app? App Clips allow users access to certain parts of product or service apps without downloading the full app through scan of App Clip code, QR code, or NFC tags. To leverage this capability, developers need only to create a clip under 10MB in size and ideally use sign-in with Apple and Apple Pay to ensure the user won’t need to login or create an account.  
  • Updated Messages: The new features for Messages offer better management, control, and communications. With the new release, users will be able to pin messages, seamlessly keep up with group threads, and further customize images, emojis, and Memojis.  

Most of the new capabilities in iOS14 apply to iPadOS 14, so best to focus on what’s going to make the iPad experience even better and users more productive and creative. These include:  

  • New Search Tool: With a new streamlined user interface, search on the iPad will look and perform much like that on a MacAdditionally, search on iPad will be more comprehensive and capable of finding anything on your device or on the internet. This indicates further convergence between the iPad and the Mac.  
  • Scribble: Apple Pencil users will be thrilled to know that their handwritten text will now automatically convert to typed text when scribed in any text field. Write a response with Apple Pencil to a text in Messages or a keyword search in Safari. Additionally, on-device machine learning will enable the iPad to distinguish between handwriting and drawing. Notetakers can now easily select, cut, and paste handwritten notes as typed text in documents.  
  • Enhance Augmented Reality: A new ARKit will allow developers to create augmented reality apps with such depth and precision that experiences will seem even more real. The capability will impact the ability to accurately depict virtual try-on experiences, interior design, and more. And the introduction of Location Anchors will allow developers to pin their AR experiences to a specific location anywhere in the world.  

The Bottom Line 

Picture a line representing user experience with Mac at one endpoint and iPhone/iPad at the other endpoint. Now, picture each endpoint moving toward one another, inching closer and closer togetherThe implications point to complete convergence in the future across Mac, iPad, and iPhone experiences. And while these will continue to be separate products with different use cases, it signals a warm welcome for those users who have use for two or all three products 

For Apple, getting consumers to buy into the ecosystem is what it’s all about. Luckily, the experience will be seamless for the consumers who do.