Face recognition is not a new feature. But it’s one of the most important use cases of Artificial Intelligence today. Organizations use face and image recognition features to deliver memorable digital user experiences to people. The face recognition feature is quite common nowadays in the form of mobile biometrics. Image recognition features are more prevalent in social media networks.

The market for Face/Image recognition is growing evidently, and is estimated to be close to $10 billion by 2023. With AI powering it, the technology can recognize people just like a human brain can. Back in the days, it wasn’t easy for mobile app developers to implement this feature in web, mobile or desktop apps. But now, there are qualitative APIs that can help them get it done effectively.

Here are a few popular ones that app developers should try out at least once.

Amazon Rekognition

Amazon Rekognition is an image/video recognition tool that can analyze image and video files in Amazon S3. The API is powered by deep learning technology developed and owned by Amazon.

Key features include:

  • Face recognition in videos and images
  • Facial analysis to determine emotion, age range etc.
  • Text detection in images
  • Unsafe content detection
  • Object/activity detection


Kairos is another popular face recognition API that utilizes a potent combination of computer vision and deep learning to recognize faces in videos, images, and in the real world.

Key features include:

  • Face recognition and identification
  • Gender and age detection
  • Multi-face detection
  • Diversity recognition

Microsoft Computer Vision API

A widely used API developed by Microsoft, the Computer Vision API can process visual data in real-time and possesses machine-assisted image moderation capabilities. Developers use this API to implement a feature that lets the application identify people who were previously tagged in images.

Key features include:

  • Image analysis
  • Real-time video analysis
  • Text detection in images
  • Read handwritten text in real-world environments
  • Recognize over 200,000 celebrities and landmarks

Watson Visual Recognition API

The Watson Visual Recognition API deserves to be on this list as it’s a favorite for a lot of developer communities in the web. The API enables tagging, classifying, and searching visual content using deep learning algorithms. Another great feature is that the Watson Visual Recognition API can be integrated with Core ML to build complex iOS apps with computer vision and analytics capabilities.

Key features include:

  • Analysis of object, face, explicit content etc.
  • Pre-defined image analysis models
  • Creation of custom models that can be trained

Google Cloud Vision

Google Cloud Vision features a number of pre-trained models for impressively accurate image recognition and analysis. The API allows developers to build use case-based custom models using AutoML Vision.

Key features include:

  • Wide array of pre-trained models ranging from transportation to wildlife
  • ML Kit integration for Android iOS app development
  • Handwriting, face, and landmark detection
  • Explicit content detection
  • Sentiment analysis


Even with the right tools and APIs, implementing face and image recognition features in apps is a complex process. In addition, this also requires trained AI/ML models. To conclude, all this and more can be done only if the developer has great expertise in leveraging AI and app development. AOT has proven expertise in developing complex AI-driven mobile apps for Android and iOS platforms. If you are planning to utilize a next-gen app with facial/image recognition features, we can help. Get in touch with us to check out our track record.

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Artificial Intelligence is now seen as a critical component for enterprise digital transformation. AI can take many forms to help businesses achieve accelerated growth and improved operational performance in highly competitive markets. Among the various forms of AI, voice interfaces are probably the most demanded. For businesses, an AI-driven voice interface is known by a different name – Conversational AI.

The benefits of conversational AI seem to be expanding without limit as AI continuously evolves. From improving customer service to increasing online sales revenue, the wide array of benefits granted by conversational AI are what businesses are willing to invest in.

As a matter of fact, Gartner predicts that by 2020, 25% of customer support & service operations will be driven by virtual assistants across various engagement channels.  

So the question now isn’t about how a company should implement conversational AI. It’s more about where they should start. That said, this blog covers a few major aspects that make a great conversational AI app. Make sure your journey to implement and leverage conversational AI is along the right course.

Implementation efforts centered on the business case

Before you start implementing conversational AI for your business, make sure that you can articulate the business case as well as the returns you expect from the technology. If you aren’t sure about the latter, work collaboratively with your AI services partner to help define the value you would like from your conversational AI.

With your efforts centered on the business case and the expected value, it’d be easier for you to deploy the right technology by making the right design decisions. The key is to stay true to the defined goals. As long as you are sure of the desired result, you can shape the implementation properly while taking into account the business constraints such as time or budget. This way your tech will only possess features that your business really needs.

A UX that streamlines customer journey

The AI-based solution’s adoption and revisit rates depend on a number of factors out of which the UX is possibly the most important. Even if the conversational AI solution features attractive dialogs and integrated data that facilitates personalized responses, it would still appear underwhelming if it lacks a solid user experience.

For a conversational AI, the UX needs to take a few major factors into account. The solution should speak and converse like a human. This doesn’t mean it should pretend to be one. Users may expect digital customer support employees to participate in small talk, remember, understand context, and be smart.

Many companies try to use chatbots that pretend to be humans when interacting with potential customers. This is likely to backfire as the approach can break the trust the end users have on the solution. The conversational app should be able to resolve the user’s issue. If it fails to do so, the user will approach a different channel with a better alternative that can serve them better.

Information pertaining to things that are out of scope for your intelligent bot should be provided early on in your customers’ journey; at least as links. The personality of the bot is also important. It can be humorous, sassy, or formal with a rich UI coupled with modern design standards.

The key is to have the conversational app cover the user journey from the beginning to the end while drawing out a satisfactory conclusion for users with their issues resolved.

A scalable platform that can handle multiple intents

When going for an AI-driven conversational bot, it’s best to start small. Implement the idea as a small project and develop it further in phases after assessing the initial results. Choosing a scalable platform here is a great approach that helps you capitalize on your initial investment and gives you a lot more options when moving forward.

The user will want to achieve something with the app, right? There will be an ‘intent’. You should ask yourself how many intents your chosen platform can handle. Various conversational AI platforms have different capacities when it comes to handling intents. The platforms will have different algorithms to process the intents. Though Machine Learning capabilities make it easier for conversational AI applications to learn intents, such features still won’t be enough if there are hundreds of intents over multiple business departments, divisions, languages, and geographic regions.

There are platforms that are powerful enough to deal with a large number of intents but there tends to be a limit at present which forces enterprises to develop multiple solutions to get by. The bottom-line here is that the conversational AI platform should be capable of delivering precise responses depending on user intent and context. It’s not easy but it is practical provided the chosen platform is highly scalable.

Top-notch security and encryption standards for customer data

Conversational data can be a goldmine in disguise for businesses. Such data can be analyzed and understood much easier than the data gathered from calls. However it also presents a great risk to organizations. They will need to secure the data and ensure the privacy of their users. Any compromise in the conversational data could end up causing your customers to lose trust in your business instantly.

The AI solution should be capable of anonymizing the data while also making it easier to understand the intents for analytics. The identifiable data can be replaced with placeholders. This way the customer identity will remain anonymous while the intent will be clear to your organization for analytics purposes. If there are transactions involved, the application should have robust encryption mechanisms to encrypt the data that would be transmitted over the internet.

Your organization should be certain about the information you wish to collect from customers, the security practices that can be implemented to safeguard the data, and practices to make good use of the data without compromising the privacy of customers even before building a conversational AI application.

The value of the AI solution depends on its capability to continuously learn and improve. Building one such solution requires great proficiency in AI and associated technologies and years of experience in building enterprise-grade business solutions. AOT has both, and we can help you utilize a powerful conversational AI program as well. Talk to our experts today to learn more.

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Google stands out among all the corporate giants actively attempting to leverage artificial intelligence efficiently, evident from their recent I/O where they revealed that Google AI is now capable of making real life conversations – a disruptive evolution of AI that’s turned many heads this year. Of course, the advancement would soon enhance mobile experiences which is exactly what modern consumer lifestyle demands.

Consumers today spent more time on mobile devices compared to televisions or desktops. This is reflected particularly in eCommerce, with many studies finding a steady growth in mCommerce sales.

Statista projects global mCommerce revenues to exceed 660 billion US dollars this year.

mCommerce is the key to the success of eCommerce retailers today, and optimizing it with AI minimizes associated risks while boosting the business’ growth. AI-infused mCommerce sites get a number of cool features – from automated reasoning capabilities to advanced customer behavior patterns analyses and predictions.

That said, here are a few ways how eCommerce businesses can optimize their mCommerce with Artificial Intelligence.

Personalized recommendations

Basically, what you always see in mCommerce apps like Amazon. You get recommendations based on your browsing patterns and previous purchases. For a mCommerce business, upselling and cross-selling are great ways to boost sales by enticing existing customers. This can be performed by powerful AI solutions that analyze customer behavior, preferences, and footprints to figure out the products that they would be interested to check out.

Personalized push notifications

Push notifications are sometimes that which triggers a purchase from a potential buyer. For instance, buyers who had to leave before checkout can be reminded of their abandoned cart with push notifications later. AI engines can do this and more. They can identify customer preferences and use tailored push notifications to target prospects who are more likely to buy certain products, and also re-engage existing customers.

Virtual assistants

Over the years, virtual assistants have become smarter and more versatile facilitating result-oriented, fluent interactions. Virtual assistants make a great addition to mCommerce platforms providing a much more convenient, easy, and interactive shopping experience to customers, and increased conversion rates for the mCommerce brands. With AI-driven virtual assistants, customers need only launch the app and ask the assistant to place an order for a particular item.

AI-powered chatbots

Unlike the virtual assistant, chatbots are present to provide support to customers who came across some kind of issue while trying to make a purchase. Many customers are likely to have queries regarding the products they intend to buy. In a modern mCommerce store, chatbots should be there to answer customer queries, and offer human support if necessary.

There are many AI solutions in the market today like Botsify, Agent.ai etc. that allow brands to create and train their own unique chatbots easily and integrate them into their mCommerce site. These chatbots have self-learning capabilities that make them better as more data are fed to them.

AI-based price negotiation

Shopping cart abandonments are one of the biggest problems eCommerce stores encounter. One of the major reasons for such issues is the fact that users cannot negotiate product prices in a mCommerce site unlike brick and mortar stores. As an alternative, users tend to research competitor stores to see if the item they seek is available with a smaller price tag.

The advancements in AI today can present solutions to this particular problem in the form of an AI-based mediator system. The solution can interact with the prospects, analyze their purchase histories and interests, and determine a strategic discount offer they’d be interested in. This can get reluctant prospects back on board and finish a purchase.

Voice & image search

If it’s an eCommerce store, there must be a ‘Search’ option, especially if the store has thousands of products under various categories. The traditional text-based search option is almost outdated now, and is being reinvented by harnessing AI. AI-powered voice search and image search features have already started taking over, and are being welcomed with praise by online shoppers.

Voice search services like Inbenta, Klevu etc. recently gained much momentum across eCommerce platforms. Though not always accurate, AI-driven voice search feature has a 95% success rate according to studies.

Traditional image search, on the other hand, has evolved into visual search today thanks to AI. There are machine learning algorithms available that can interpret visual data with impressive accuracy. This essentially redefines the shopping experience of mCommerce customers. All they need to do is use their mobile cameras to snap pictures of an item they see in real life. The AI-based visual search algorithm can then match objects in the image, and perform a search for the item in the image on the linked database.

AI-AR powered visualization capabilities

Augmented Reality entered the scene when mCommerce brands started looking for ways to reshape mobile shopping experience for customers. AR, by that time, already made its mark in the form of Pokémon GO. Now the trend is to combine AR with AI resulting in interactive AR applications with an intelligent AI core.

Deloitte’s Digital Identibot managed to combine both technologies to deliver an impressive experience, though the solution isn’t commercially available yet. The point is that today it’s possible to combine AI and AR thanks to a plethora of readily available tools, frameworks, and platforms.

The AR development community is steadily growing, and people keep coming up with creative ways to use the technology in various industries. Google’s ARCore and Apple’s ARKit are two great SDKs that can help aspiring AR developers build innovative applications for eCommerce stores.


Ease of use is what made mobile devices so important for people across the globe, and ease of use is what they expect when doing something on said devices – be it seeking information or shopping. Prioritizing mobile users’ convenience and securing their transactions are the key for a mobile app’s success. For mCommerce stores, all of this and more is possible by harnessing artificial intelligence.

If your mCommerce site still hasn’t channeled AI yet, it’s time to rethink your mobile sales strategy. AoT technologies can help you wield AI the right way so your brand always stays ahead of the curve. Give us a ring to learn more.

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We live in an age where technologies have evolved big enough to make revolutionary changes in various industries across the globe. Businesses today enjoy the luxury of powerful technologies that can trigger digital disruption in a short time, provided they are simply wielded well. Among the hottest technologies today is Machine Learning (ML) – something that’s become invaluable for growing and established businesses.

With ML, businesses will be able to uncover insights from the data they generate, and predict outcomes which subsequently leads to remarkable changes in the way they function and grow.

The technology has been around for a while, and is constantly evolving to become more sophisticated. Yet, its full potential has not been explored beyond self-driving vehicles, fraud detection, and predictive analytics of retail trends.

Nevertheless, it’s set towards a future with tremendous impact in our world.

Here are a few interesting forecasts on where machine learning is headed to.

Improved algorithms

ML makes use of unsupervised algorithms to perform predictive analytics on datasets when only input data is available. Supervised learning is when the output variables are already known, which makes unsupervised algorithms quite close to the concept of artificial intelligence. The machine itself learns how to identify complex processes and patterns without direct human intervention.

Unsupervised algorithms can find hidden patterns, groupings, and more which wouldn’t be possible if they were supervised. The approach is already in practice, but we will see great improvements to unsupervised algorithms in the coming years resulting in more accurate predictions.

Quantum computing would be adopted more

ML employs a number of classical techniques that can be enhanced by leveraging quantum computing and its benefits. Quantum ML algorithms are potentially capable of triggering a major evolution of machine learning resulting in faster data processing and faster information synthesizing. Drawing insights would be much easier with quantum computing facilitating heavy-duty computational capabilities.

Advanced cognitive services

Present day cognitive services consist of many components including machine learning SDKs, APIs etc. which allow developers to make their applications smarter. Intelligent applications will be able to carry out complicated tasks like vision recognition, speech detection, speech understanding etc.

With the technology constantly evolving, we can expect advanced cognitive services in the form of highly intelligent applications that will not only be able to speak, hear, and understand but also reason with the situation and interpret users’ needs effectively.

Advancements in robotics

Machine learning and AI are what’s going to drive robots in the future. In the coming years, the advancements in machine learning will lead to increased use of robots. The robots would obviously be smarter with self-supervised learning, multi-agent learning, and remarkable cognitive capabilities. They will be able to accomplish more complicated tasks, and will go mainstream in a short time.


Still considered to be nascent, machine learning is inarguably one of the most disruptive technologies in the world today. The forecasts mentioned in this blog explore only just a fraction of ML’s potential. The complexity of the technology and the difficulty in comprehending a great ML adoption approach make many businesses reluctant to use machine learning. But it’s all about to change soon.

If you have queries regarding ML or need an ML-based digital solution, feel free to start a conversation with our experts.

While the rapid evolution of technology is proving to be of great benefit to businesses, it’s still not easy for them to cope with the not-so-subtle changes or overcome the plethora of challenges. Keeping pace with evolving technology in itself is not easy, even for bigger businesses. In addition, they need to figure out effective and optimal ways to deal with the large amounts of data involved in their IT operations, changing customer demands, process inefficiencies etc.

Out of all these aspects, it’s the data that hold the key for a business’ rapid growth on par with evolving technologies and dynamically changing market conditions. But when enterprises keep relying on traditional data monitoring and management systems, the increasing volumes of data would pose a problem. Moreover, they’d be unorganized and insensible for the most part, leaving the enterprise’s data management staff to do some heavy lifting.

To deal with these issues around data, data management, or rather ‘service management’ in an enterprise, modern-day technology grants intelligent computing mechanisms and practices.

…and this is where AIOps originated.

But what is it?

AIOps or ‘Artificial Intelligence for IT Operations’ is a term popularized by Gartner. It’s a combination of data analysis and machine learning technologies designed to make internal management systems of enterprises more sensible or ‘smart’.

Why businesses need AIOps

Many businesses already prepared for the spike in data in advance, and set up various systems in place to handle the data flow on a daily basis – from project management methodologies to sophisticated IT management systems. The existing monitoring systems are capable of alerting the management when issues arise. But the problem arises when there is just too much data, and the system starts generating thousands of alerts every minute. The personnel are in for major persistent headaches.

In this scenario, AIOps platforms would combine big data and AI/ML functionalities to either enhance or partially replace a number of IT Operations tasks and processes which include performance monitoring, event identification and correlation, analysis, service management, and automation.

So essentially, AIOps adds one more layer over the enterprise’s platforms – something much smarter, and capable of simplifying operational and managerial tasks.

Here are a few notable advantages.

  • No need for organizational silos – In enterprises that generate overwhelming volumes of data, procuring relevant real-time data for analysis can be tricky. But if the AIOps platform is directly linked to the project/data management platform, it can gather necessary data and derive insights without the need for any silos.
  • Prioritizes issues for easier management – AIOps, unlike traditional systems, analyzes data to understand and prioritize issues, and then highlight issues based on their priorities to the management. This way, the authorities can focus on the issues that require immediate attention rather than putting a lot of effort assessing threat levels and risks to categorize issues.
  • Solves problems automatically – Doesn’t apply to all problems, but it’s still a highlighted capability of AIOps. Every time an issue is solved, the AIOps-based system records the methods involved. It understands the type of problem and an optimal and effective solution, making things easier for the IT staff. The system can be automated to solve certain specific recurring problems.

Digital transformation with AIOps

The world sees digital transformation of enterprises as a shift from legacy systems to modern dynamic frameworks that facilitate improved business agility, faster growth, and better decision-making. But when you look at it closely, you will notice that major changes from digital transformation take place particularly around three key areas – the business model, customer experience, and operational process.

AIOps, in essence, can be linked to these areas to generate insights from the data collected from them. Leveraging both big data and AI helps enterprises eliminate repetitive tasks, and makes internal systems more adaptable and responsive to change. Because it predicts sources of potential risks or threats, the enterprise can proactively plug security gaps and minimize risks. This action can also improve customer satisfaction.

To conclude, AIOps facilitate Continuous Integration and Continuous Deployment (CI/CD) for IT functions, making it a great addition to an enterprise’s accelerated IT ecosystem.


Despite Gartner’s deep analysis on the platform, AIOps still hasn’t gone mainstream yet, probably due to the fact that IT Ops team are still hesitant to use new-gen technologies that replace the ones they are already familiar with. However, the hard truth is that IT ecosystems of enterprises will inevitably change as long as technology continues to evolve.

Resisting the change would only do the enterprise more harm than good in the long run. AI and big data have already proven their mettle individually. Coupling them both is what AIOps basically does, and it can guide the enterprise to a better future through better decision-making and operations.

You can get AIOps and other similar AI-driven solutions from AoT Technologies, as we’ve been helping enterprises leverage these technologies for a while now. Interested in knowing how a custom machine learning-based solution can secure your business or empower it? Contact us now.

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