Blog Category: AI/ML

What if your phone or smartwatch could process AI tasks instantly, without needing to connect to the cloud, saving time, and keeping your data private?

That’s exactly what LiteRT — Google’s high-performance runtime for on-device AI delivers. Formally known as TensorFlow Lite, LiteRT is the next breakthrough in Machine Learning, bringing faster, smarter and more efficient AI to devices. 

Whether you are building next-gen AI/ML apps or embedding intelligence into hardware, nosedive into the real benefits and the trend setting use cases of TensorFlow Lite.

TensorFlow Lite For Low Latency, Offline, On-Device ML

LiteRT is built for real-world, on-device ML (ODML). It is super optimized to address the common hurdles developers face when developing AI on mobile and embedded devices. 

For instance, you want to add a smart feature to your app like recognizing images or understanding spoken commands, without relying on a suitable internet. LiteRT helps developers run ML models right on the user’s device, making the functionality faster and more efficient. 

That’s not all! Let’s break down why TensorFlow Lite sparks as a highly useful and demanding framework for integrating on-device AL and ML capabilities.  

Why is TensorFlow Lite the Future of Mobile Intelligence?

The riches of TensorFlow Lite isn’t just theoretical, it’s changing the game in real world applications. Here are a few jaw dropping benefits of TensorFlow Lite to explore the possibilities of on-device ML functionalities that help users access ML services in the app with minimal use of the internet. A lot more is possible with this framework. Check it out.    

No Latency Instant Results

Because LiteRT runs everything directly on the device, there is no need for a round trip to the Cloud. It helps your AI models process data on the spot, without any delay — whether it’s image classification, face detection, voice recognition, or predictive text. 

Offline Capabilities 

You can use Machine Learning features without needing a constant internet connection, which is perfect for users on the go. TensorFlow Lite is highly used to build apps that need to work in remote areas or for users on the go who don’t always have reliable access to the internet.  

Tiny Models, Big Impact

TFLite reduces the size of models and binary files, making it perfect for devices with limited storage and processing power. Think of smartwatch, fitness tracker, surveillance apps, or any other IoT device — LiteRT helps run AI tasks smoothly, even in small, resource-constrained environments. 

Your Data Stays Private

With LiteRT your data never leaves your device. Since no external servers are involved, you can rest assured that your data is secured and your privacy is protected. This is a huge advantage, especially for apps handling sensitive information like healthcare, fitness, or location.  

Cross-Platform

Whether you are developing Android, iOS, or IoT devices, TensorFlow Lite works seamlessly across platforms. It’s adaptive and versatile, making it easy to integrate with mobile apps, embedded systems, and edge devices. 

Read More: Future of Cross-Platform Development with React Native in 2024

Power-Efficient AI

TensorFlow Lite is designed to run Machine Learning models with minimum power consumption. By eliminating the need for constant network connections, it helps extend the battery life of mobile devices and embedded systems.

Ready-To-Run LiteRT Models and Multiple Framework Support

LiteRT makes it easier by offering a library of ready-to-run models for common Machine Learning tasks like language processing, image recognition, and more. But that’s not all – LiteRT also supports conversion tools that allow you to bring in models from other popular frameworks like TensorFlow, JAX, PyTorch, and more. 

Using AI Edge conversion tools, developers can quickly optimize and convert these models into TensorFlow Lite format, making them lightweight and efficient for on-device use.

TensorFlow Lite Use Cases For Building Future-Ready Machine Learning Apps 

The real curiosity arises when you explore what LiteRt can do. Here are a few ways it is already transforming everyday experiences. 

Gesture Recognition

Have you ever used your phone to recognize a swipe, wave, or tap? TensorFlow Lite can help apps recognize these gestures in real-time. This makes the user interface more intuitive, allowing for smooth navigation and a better overall experience. 

A series of steps include recognizing and gathering sensor data when gestures are performed, using this data to train the ML model, converting the model into TensorFlow Lite format, and integrating this format into the mobile app.   

Image Classification 

Heard of an app that can identify objects in photos instantly? TensorFlow Lite enables mobile apps to classify images using pre-trained models. This automates various tasks in multiple fields like retail apps that can recognize products or healthcare apps that analyze medical images. 

With a series of steps which include obtaining a pre-trained model and converting it into TensorFlow Lite format, integrating this format into the mobile app, input images preprocessing, and smoothly running the interface on the model, developers can leverage ML and AI on mobile and embedded devices. This helps build intelligent and innovative mobile apps that can classify and analyze the Images.  

Speech Recognition

With TensorFlow Lite, developers can build apps that understand voice commands and convert spoken words into text instantly. This is a game-changer for virtual assistance and transcription apps, making them more efficient and user-friendly. 

The steps include gathering speech data as audio files and labeling them with corresponding text, helping the model learn the relationship between sounds and words. This data is used to train the ML model to recognize the audio signals and map them in text. The trained ML model is converted to TensorFlow Lite format, preprocessing the audio input and running the model effortlessly.

Object Detection

TensorFlow Lite is used to build mobile applications that can detect and locate multiple objects in images or videos. This technology is highly useful for security applications like surveillance, in retail for managing inventories, and in a wide range of niched business use cases. 

A series of steps include training your own object detection model using annotated images or pre-trained models, converting this model into the TensorFlow Lite format, and integrating it into the mobile app. 

Pose Estimation

TensorFlow Lite is highly used to build next-gen apps for fitness and gaming. It helps apps detect human poses in real-time from images and videos based on the user’s movements, creating an interactive and fun experience.

The steps to integrate pose estimation capabilities involve using a pre-trained pose estimation model, converting it into a TensorFlow Lite format, and integrating it into the mobile app. 

TensorFlow Lite Simplifying AI Development For Everyone

Ready to turn your apps into intelligent systems that think on their own? With TensorFlow Lite the future of mobile machine learning is already here. Let’s explore some of its demanding applications. 

Smart Cameras

From instant object detection to advanced photo editing, LiteRT enables powerful image processing right on your device. Think about the AR filters, face recognition, or real-time object tracking, all without needing Cloud support. 

Wearable Tech

Health monitoring devices and fitness trackers rely on TensorFlow Lite to analyze motion, detect heart rates, and deliver personalized health insights, all while consuming very little power. 

Voice Assistance And Smart Devices

With LiteRT’s ability to process voice commands in real-time, your smart home assistance can become faster and more responsive even offline. 

Healthcare Software

The Medical Imaging apps can now analyze data directly on the device, allowing for faster diagnoses in areas with limited connectivity. Be it scanning X-rays or monitoring vital signs, LiteRT opens the door to real-time healthcare solutions.

Building Intelligent Apps Has Never Been Easier

Whether it’s automating tasks, enhancing app performance, or creating entirely new functionalities, the possibilities with TensorFlow Lite are endless. 

TFLite’s simple architecture makes it easy to convert and optimize existing models for on-device use. Plus, with the pre-trained models available, developers can quickly get started on building intelligent apps that don’t just react, but smartly adapt in real-time. 

The TensorFlow Lite framework also provides tools like GPU acceleration, and model quantization to further enhance performance, reducing the size of ML models without compromising accuracy. It brings cutting-edge ML right into the heart of mobile and IoT innovation.

The reason why Python is extensively used and considered one of the top-used programming languages in 2024 is its ease of use, clean and readable syntax, extensive 3rd party integration capabilities, and wide range of free libraries.

In fact, the design philosophy of the Python programming language focuses on code readability through exclusive use of whitespace. On the flip side, its object-oriented approach helps developers write clean and logical codes for small, medium, and large projects.

This has made the Python programming language a staple in data science and machine learning allowing developers to use the language to create data visualization, statistical calculations, manipulate and analyze data, build foundational models, and more.

Additionally, Gen AI led the rise in its adoption and scaling, which highly earned the business value for the takes, shapers, and makers who used the AI and Machine Learning models customized with proprietary data or built from scratch.

Even if you haven’t worked closely with ML models, this blog is a detailed guide that will save your time in exploring and selecting the best Python libraries for Machine Learning projects. It also covers answers to why decision makers like you choose Python as backend technology over others.

Top 5 Python Libraries For Machine Learning in 2024

Being one of the trusted Python development companies, TOPS has explicitly researched and has come up with the list of best Python libraries for Machine Learning after considering its usage, use cases, and popularity. Scroll down to explore the capabilities and implementation of these Python libraries for ML models.

1. TensorFlow Python

Python’s Machine Learning Library built by Google, TensorFlow Python is popular for what’s called ‘differentiable programming’. It automatically computes a specific function’s derivatives with the programming language. With TensorFlow, users can easily evaluate and develop Machine Learning and Deep Learning models through its flexible framework and architecture. It’s easy to leverage TensorFlow Python to visualize ML models on both mobile and desktop.

Reasons for TensorFlow’s Popularity

• Building, training, and deploying ML models.
• Makes simple data graphs out of complex computations.
• Flexibility with deep and recurrent neural networks.

Capabilities of TensorFlow

• Users can divide work into multiple GPUs or CPUs, allowing developers to benefit from parallel computing.
• Easy to build neural networks as it offers functions to activate, built layers, and loss functions.
• Interactive visualization of model graphs and training metrics helps in model visualization through the use of TensorBoard.
• Seamless deployment of TensorFlow models on environments like Cloud, web, and mobile for easy scalability.

Use Cases of TensorFlow

• Object recognition, facial recognition, medical imaging, and quality analysis in manufacturing.
• Language translation, text classification, and sentiment analysis.
• Speech and translation recognition.
• Unique recommendation systems.

Apps Built Using TensorFlow

• Google Photos
• Google Voice Search

import tensorflow as tf

# Initializing constants
x = tf.constant([2, 4, 6])
y = tf.constant([1, 3, 5])

# Addition
res = tf.add(x, y)

# Initializing Session
sess = tf.Session()

# Result
print(sess.run(res))

# Closing Session
sess.close()

2. PyTorch

PyTorch is highly popular for its use in large and dense data sets or graphs. It is an open-source ML Python library that is based on Torch, a C programming language framework. This Python library is used in Machine Learning applications, especially for natural language processing and Computer Vision. PyTorch is a deep-learning Python library built by the Facebook AI research team.

Capabilities of PyTorch

• Helps users execute multi-dimensional operations and supports broadcasting and slicing.
• Extended data sets are possible for scalable Deep Learning models. You can leverage efficient distributed computing that offers distributed communication, optimization, and data loading.
• It offers tools for seamless deployment and integration into development environments.

Use Cases of PyTorch

• Solve tasks related to gaming features, decision making, and robotics that are controlled through reinforcement learning libraries.
• Leverage pre-trained models like GPT, BERT, or ResNet to streamline particular tasks.

Apps Built Using PyTorch

• Samsung Electronics
• Apple

3. Keras

Designed specifically for building neural networks for Machine Learning models, Keras becomes fully functional to train neural networks with minimal coding. It builds and evaluates neural networks with ML and Deep Learning models.

Keras is Popular For

• Works across multiple environments with flexibility and portability.
• It is extensible in nature.
• Easy integration with activation functions, optimizers, layers, and objectives.
• Mostly used for agile prototyping.

Capabilities of Keras

• Offers easy functions and tools for data augmentation techniques.
• You can build your own neural network model through custom activation and loss functions.
• Seamless evaluation of trained models and offers required tools for calculating precision, F1 score, and recall.
• Helps users leverage the potential of GPU for boosting inference and training resulting in GPU acceleration as well as distributed computing.

Use Cases of Keras

• Streamlines generating creative content, data augmentation, and image synthesis tasks.
• Simplified speech recognition.
• Speedy object detection.
• Smooth image classification such as identification of objects from images.

Apps Built Using Keras

• Netflix
• Uber

4. Scikit-Learn

Scikit-Learn’s simplified design makes it a user-friendly library for those new to ML models. It ensures engaging UI Machine Learning tasks. It is unique among the popular Machine Learning libraries of Python. It makes it easy for classification, clustering, dimensionality reduction, regression, and model selection.

Capabilities of Scikit-Learn

• Smooth functionalities for handling data, managing missing values, feature extraction techniques, and encoding categorical variables.
• Allows searching structures, relationships, and patterns with unlabeled data.
• To make predictions, it helps models learn from variable data sets.

Use Cases of Scikit-Learn

• Allows predicting class labels through algorithms such as random forests or gradient boosting methods.
• Implementation of decision tree regression, linear regression, and polynomial regression.
• Helps leverage clustering algorithms to discover patterns and for seamless classification of identical data points without class labels.

Apps Built Using Scikit-Learn

• Inria
• Spotify

5. Matplotlib

It is a Python library specifically developed to visualize data through tools that build static, interactive, and animated visualizations. This involves creating beautiful graphs, bar charts, plots, histograms, and more. This is popularly known for data visualization patterns. It has modules that make plotting easier through tools for controlling font properties, line styles, and more.

Capabilities of Matplotlib

• Helps showcase different bar sizes for data comparison.
• Swift visualization of the relationship between two attributes to explore the trends, changes, and patterns.
• Seamlessly add text, labels, or annotations.
• Create pie charts to represent % as a whole with effective personalization in labels and colors.
• Data visualization in a three-dimensional landscape.

Use Cases of Matplotlib

• Histograms
• Geographic maps
• Heatmaps

# Program to form a linear plot

# Import packages & modules
import matplotlib.pyplot as plt
import numpy as np

# Initializing data
x = np.linspace(0, 4, 10)

# Plotting data
plt.plot(x, x, label='linear')

# Adding legend
plt.legend()

# Showing plot
plt.show()

In order to select the right Python libraries for your Machine Learning project, you need an expert consultation that helps drive your software development project in the right direction using the latest tools and agile methodologies. And TOPS has rich experience working on complex Python projects, offering new-age Python development services.

We have solved some of the unique business challenges by building next-gen Python apps

• Our Python team has built an Infrastructure as a Code (IaaC) platform where the users can register on the platform, choose the plan, and provide Amazon marketplace details. Once users add the details, the platform generates AWS infrastructure in the backend for the user, based on their requirements. We have used Cloud Formation, RDS, Lambda, EC2, SQS, S3 from AWS and Boto3 to access AWS via Python code.
• An app that controls product reviews with automated discovery of negative /positive reviews through the use of Python libraries like SKLearn, TF-IDF, Pandas, NumPy, Django, and Django Rest Framework (DRF).
• By using the Azure AI, Django, and Django Rest Framework, we generated an OCR model that fetched data from the parking fines receipts.
• Built a solution where users can upload their documents like PDF, Doc file, and other formats. The ML models fine-tune the text inside the document and users can ask questions that the model answers based on the fine-tuned text. We have used FastAPI, Transformers, Watchdog, Cryptography, LlamaIndex, Gradio, PyTorch, and Sentence-Transformers for this project.

Explore how TOPS Infosolutions can be your extended tech team to handle any technology complexities for your software development needs.

Why TOPS as Your Trusted Software Development Partner?

We have a rock solid project management process in place to meet our client’s needs, minimize project risks, and avoid budget overruns. What’s most significant is our consistency in staying abreast with the latest technologies and trends. Hire Python developers who have gained tech excellence and worked on time-tested development processes for optimum outputs.

Business Intelligence (BI) and Artificial Intelligence (AI) are tools that are often misunderstood in the enterprise setting. Business Intelligence and artificial intelligence are widely popular and imperative.

Artificial intelligence travels around the usage of computer systems to imitate different attributes of human intelligence. It includes learning, problem-solving, and judgment. Although it is still in the inception stage, businesses are already witnessing massive potential in AI for decision-making, speech recognition, and everything in between. AI and BI have essential and overlapping enterprise-related applications.

Business intelligence solutions talk about the various technologies and tools used to gather and analyze business data. The primary purpose of BI is to offer companies with valuable information and analysis to help in decision-making. By making use of BI, businesses can make decisions approximately five times quicker than they could otherwise.

This article throws some light on the differences among each field and how they can work in harmony in the future.

The Differences: Goals of BI and AI

Goals of BI

• BI targets to make the process of accumulating, reporting, and analyzing data more efficient.
• It lets companies enhance the data quality they collect and maintains consistency.
• Business Intelligence solutions help to convert raw data into an understandable picture. Still, they are not designed to deliver the way in which that data has to be used in decision-making.
• Many leading companies use BI tools for a variety of business purposes, like HR, sales, and marketing.
• It monitors every business operation on a regular basis and utilizes data to generate spreadsheets, dashboards, performance metrics, graphs, charts, and other insights.

Goals of AI

• By modeling human behaviors and thought processes, AI programs can learn, adapt and make rational decisions.
• Using AI-driven applications, like chatbots, can boost efficiency and bring in more significant profits.
• AI can offer human operators recommendations and can act autonomously.
• In contrast to BI, which analyzes data but leaves the decision-making process to humans, AI can empower computers to make business decisions.
• AI chatbots can answer customer questions without human intervention.

Use-Cases: BI vs. AI

BI Use-Cases

Many companies also use BI ubiquitously and to better understand their customers. Businesses communicate with their customers through various interfaces, such as emails, social media, and chatbots. BI tools are capable of collecting customer data from these disparate sources and deliver it in a unified and cohesive format. By gathering and combining such data from these sources, businesses can better understand their customers and find better ways to serve them.

Companies also utilize business intelligence solutions to enhance operational efficiency. BI tools can keep track of the key performance indicators in real-time, allowing businesses to recognize and solve problems quicker than they otherwise could.

Some of the general BI applications are spreadsheets, data warehousing tools, data visualization tools, and reporting software.

AI Use-Cases

The use-cases of AI enterprise are widespread, from refining medical diagnoses to designing useful energy grids. Artificial intelligence in web applications helps in understanding retail customers better. They generally help in delivering cognitive insight, process automation, and cognitive engagement.

Process automation is the most common and valuable type of AI-powered enterprise application. Such applications can automatically update customer information and records, manage boilerplate customer communication, and offer elementary guidance on standard documentation and contracts. These applications can swap human back-office and administrative functions, according to a Harvard Business Review. Every so often, it comes with a great return on investment.

Cognitive insight applications are more progressive when compared to process automation applications. Since they interact with users and data, they can learn and progress over time. Such kinds of applications can forecast customer behavior, offer advanced IT security solutions, and frame personalized ads.

Cognitive engagement happens directly with employees and customers alike in the case of chatbots. It can respond to internal company questions, offer medical advice or general customer service, and many more.

How interdependent are Business Intelligence and Artificial Intelligence?

BI and AI are unique but harmonizing. Both computer intelligence present in AI and business decision-making intelligence with data analysis and BI visualization go hand in hand.

BI supports companies by streamlining the vast amounts of collected data. However, clear visualizations and dashboards may not be enough. By embracing AI and BI’s convergence, businesses can convert a vast amount of data into a coherent action plan.

Their benefits include the following.

• AI can empower BI tools to render clear, valuable insights from the data analyzed.
• An AI-powered system can elucidate the significance of every data point on a granular level
• It can make human operators understand the way in which we can translate the data into workable business decisions.
• AI can result in the development of more adaptive and smarter BI tools.
• With extensive data, interaction with more users, and outcome analysis, one can know the various recommendations and analyses are beneficial and adjust.
• Rather than human software engineers, AI may ultimately provide the incremental improvements that make BI tools to the next level.

Several tech companies, ranging from well-established giants to novice startups, are looking forward to capitalizing based on this approach. IBM’s research wing is moving towards rethinking enterprise architecture and transforming business processes by uniting AI algorithms, human-computer interaction, distributed systems, and software engineering.

CIO Magazine had written about the company DataRobot that builds BI solutions by predictive modeling and machine learning. It had helped a healthcare company combine AI into its BI systems. 200+ medical practitioners and nurses can access forecasts and recommendations through tablets and smartphones from their PowerBI dashboards. It enabled the healthcare company to identify patients at higher risk and plan proactively on the treatments.

Wrapping it up

The prospect of BI will likely depend on AI in a specific capacity. Even though AI and BI differ significantly, they are more powerful together. We may witness several businesses would consider AI and BI as totally attached technologies in the upcoming days. Additionally, they may further explore and invest in utilizing the full potential and enabling businesses to overcome challenges grow to new heights.

We help develop expert solutions that can help streamline your critical workflows, increase ROI with Artificial intelligence in web applications. We make sure you get the desired BI solutions catered to meet your needs. Are you looking for Business intelligence solutions? Reach our experts to know more and to receive a free quote within 24 hours.

AI or Artificial Intelligence offers machines the ability to do what living beings can do. It includes the machines to self-train, and learn new things like humans – seeing, listening, conversing, and studying others. Today’s smartphones and tablets have more sensors compared to other computing devices. Although they can sense temperature changes, ground leveling, and even calculate the device’s real-time speed, it is not enough for AI.

AI capabilities require humongous processing power to execute Natural language understanding (NLU) or intelligent routing in the content delivery system. That is why these battery-operated devices are connected to the internet and cloud computing all the time.

AI in Mobile App Development and NLU

A mobile app is an accumulation of services from multiple third-party API service providers within a native software package, which helps fetch relevant data based on a user’s requests. Likewise, AI capabilities can be added, especially the Natural language understanding (NLU) feature. For Android and iOS mobile devices, Google Actions and SiriKit integrations are predominant. It can bring many AI capabilities into our daily apps, whether it is food or ride.

Delivering Personalized Experience with AI

A user’s intent is expressed in two ways: text and speech. Apart from the direct instructions, your routine behavior can be studied ad prompted. Say, you order lunch at a particular time. AI program can self-train and predicting your behavior at the stipulated time. The OS suggests various apps at multiple points of time. Besides suggesting, what if your smartphone notifies on discounts at your regular restaurant or other restaurants serving the food of your choice plus various offers? This extra step is offering a personalized experience to the user.
It is the personal experience that we expect from the apps we use.

Significant Benefits of Mobile App Development with AI

All businesses are thriving on Innovation. It helps the businesses to leverage the added advantage in going for mobile app development for its customers. Some famous brands like Zara, Domino’s Pizza, and Starbucks have proved that mobile app development can boost sales significantly. Let’s see what it has to offer.

• Inculcates Brand Loyalty

  To retain customers, businesses need a compelling mobile app presence. As more potential customers are in the mobile space than PC, unique mobile app developments can alone target brands to its customers. By leveraging AI, brands can set a solid foundation for their customer base.

• Forms Brand Recognition

  Winning the trust of your target audience can only drive customers to commit to your brand. Mobile app development will offer the opportunity to show your users why they should trust in your brand. User-friendly mobile app development with AI intuitive features can make the customers get involved with your app. It also helps to recall your business.

• Link with on-the-go customers

  People are always on the go; work, gym, café, you name it. Their easy access during such times is their smartphones. Nowadays, most people mobile active, and this provides a great opportunity if your brand has a mobile presence—a chance to convert your mobile app visitors into loyal customers irrespective of the location.

• Craft Personalized content

  Mobile apps can establish an emotional bond between the business and its customers through personalization. Studying customer persona, behavior, preferences, and such, you can give personalized content and show that you care. AI plays a vital role, and with tailored content, your chances to meet the specific requirements of the audience and turn them into frequent users are highly possible.

• Boosts Gains

  An increase in customer satisfaction increases sales. The happy your customers are, the more are the chances of visitors and visits to your app and make a purchase. It results in severe returns if you unveil a mobile app in addition to your website.

AI is altering the way we connect with apps. The AI engine is capable of delivering the entire flow from action to fulfillment based on the user’s ‘intent.’ It will communicate with the apps, servers, payment gateways, etc. In this current COVID-19 scenario, mobile apps with AI technology are monitoring vital health statistics to determine any ailment in its initial phase and avoid complications. Well, the future holds so much more.

For AI mobile application development services – Request for call back or a free quote

Many tech companies are continually exploring the opportunities that AI can offer. Talk about Alexa and Siri, the voice search, speech, and facial recognition are specific examples that are currently in use. Companies are going toward more personalization so that they can connect well with the audience and grow their business.

With the changing needs of the users for a more customized and feature-packed web experience, many modernizations have taken place ever since. This desire for evolved experience is making AI to gain prominence in web application development. AI redefines conventional web application experience, giving a user experience that feels like its natural, and for uninterrupted flow.

AI deals with creating intelligent solutions through machinery that can think and act like humans. So, what good is Artificial Intelligence in web development? Let’s see below.

Enhanced User Experience

The bounce rate is among the most worry-giving factors for website owners, especially for the eCommerce segment. For sale to happen, the look and feel of the site should be satisfactory. Else, the chances of the user resorting to other websites are high. User experience and effective navigation play a crucial part in any website design. AI can help in finding the customer’s mood and knowing their requirements through Cognitive analysis. This way can improve the feel of the website by meeting the interest of the end-user accordingly.

Producing Personalized Content

Personalized content is a way of retaining customers and making them linger on your page for a longer duration. Implementing artificial intelligence on your website provides the metrics necessary to create optimized content. By knowing what exactly a customer wants, the content can connect with them more effectively. Suggestions can pop up based on their interest, a similar product/ service that they were looking for, or based on the analysis of their previous activities and behaviors.

Voice search optimization

One of the flourishing trends nowadays is voice search optimization. Websites focus on connecting the majority of potential customers via mobile devices. Does it stop here with mobile compatibility? No. With the growth and evolution of voice-based search, website experience has to undergo a makeover too. AI can allow voice discussions between customers and websites, especially for virtual shopping background and e-commerce companies.

Chatbot for Better Reply Predictions

AI Chatbots are coming into the spotlight for predicting user’s reactions before simulating a conversation. Integrating these chatbots in web applications are making the conversations sound more natural than mechanical. These bots elevate the customer experience, respond accurately, and promptly. They also eliminate the wait time that users usually face through IVR customer support considerably.

Users Store Experience

Websites offering unique and personalized store experience keeps the customers coming. Such websites take client engagement and communication to another level. AI can replicate the feel of shopping with real retail sales workers who help customers in person with navigation, product choice, or service. AI proves to be essential for any business with an online presence and to connect with users at any level.

Improve your site’s user experience and grow your business by engaging better with the customers. We are a web development company USA, and we help build responsible Artificial Intelligence in web applications. We cater to various Businesses from startups to big organizations to understand customer’s requirements in a better way. Get free consultation and quote in 24 hours. Call anytime :+1 408-400-3737 or +91-7575000269

Artificial Intelligence is a still-emerging technology that is spreading its wings into various domains and finding its application in day-to-day activities. With the advancements in technology and especially with AI in web applications development, voice search feature is invariably altering the e-commerce business sector.

Quick search:

Whether getting a morning coffee or accessing information, all customers expect quick delivery without much delay. This waiting time has been considerably reduced nowadays with the trends in technology, and AI is making its mark. Voice search solutions are already popular with the youth, and almost everyone is getting adapted to comfortable living. This voice search is becoming predominant in e-commerce website development nowadays.

Enhanced interaction:

The way of interaction is becoming more natural with Chatbots. They will soon be learning how to recognize human emotions with the help of Artificial Intelligence. Interacting with a website via a Chatbot is becoming very easier for online buyers in the longer run. This process eliminated most of the communication barriers and made the shopping experience more handy and pleasant. By implementing AI, the conversation had is similar to the one had with a real shopping consultant who identifies a reaction, knows the product’s features, quality, and price. It is in prediction that the chatbots market share by the year 2012 will be close to $3.5 billion. Implementing an AI chatbot while developing a website, especially for e-commerce, is the most prevailing trend.

Relevant customer experience:

You can accustom your website to match appropriately to the user’s needs by evaluating customer’s moods and understanding their preferences in real-time by AI technology. Dynamic modification of individual customer’s websites and even by including search through the website can be carried out. Apart from that, this technology offers a wide range of possibilities:

• Display trending goods recommendations;
• Change prices at optimal durations of sale;
• Show personalized ads;
• Deliver customized video content to trigger sales.

Personalized store experience:

Customers can be in charge of a new web experience by merely making online purchases from home. Spike in online purchasing has resulted in increased user engagement and a more and more personalized user experience. This experience can be accentuated with the application of AI solutions and enhance better navigation for that particular user.

Much more effective marketing:

Being a part of Artificial Intelligence and Big Data analysis, Machine learning gives extensive opportunities for e-commerce marketers. Depending on customer behavior, technologies permit making marketing predictions like:

• What kind of right customers are the next buyers?
• What product varieties bore customers?
• Choice of device and channel for accessing your website;
• What is the suitable price threshold for the user;
• Reasonable time for the customer to buy a particular item.

Artificial intelligence is altering web development, especially with the e-commerce portals, which is only the beginning. New benchmarks will be set with Artificial intelligence in web applications development sector. If you’re looking for assistance from the experts, to integrate a chatbot, custom software development or any other AI feature requirement do give us a call:+91-7575000269

“A year spent in Artificial Intelligence is enough to make one believe in God – Alan Perlis”

The Internet has evolved a lot since its birth and it has made web development evolve as well – website building was a very easy process that involved nothing more than pages of standard text with few pictures and font variations.

Fast forward a decade; society was gifted with standard web connections which enabled websites to develop on HTML and PHP, including graphics and animated design elements. Fast forward another two decades – today with the advancement of technologies, web development consists of a mix of images and audiovisual content designed to deliver the best user experience rather than just information.

As per the recent news, the humanoid robot Sophia has been made a citizen of Saudi Arabia. Was that something ever expected? Additionally, Gartner predicts, “By 2020, customers will manage 85% of their relationship with the enterprise without interacting with a human”.

So, here the question arises, with whom will they interact? The answer is quite simple – via Artificial Intelligence (AI);

i.e. simulation of human intelligence processes by machines that includes learning, reasoning, and self-correction. For instance: Siri, Cortana, Chat-bots, self-driving cars, etc are delivering optimal user experience with the help of AI.

Today, as Web Development focuses on enhancing the user experience; small, medium as well as large enterprises are looking forward to leveraging the power of Artificial Intelligence in web applications for providing personalization and boosting their web experience.

Why is Important Artificial Intelligence (AI)?

• Automates repetitive learning and discovery through deeper data
• Adds intelligence
• Adapts progressive learning algorithm
• Achieves incredible accuracy

Let’s check out how Artificial Intelligence is changing the scenario of web development:

• Better Predictive Reply

A predictive reply is the process of interaction between users and machines. The machine fails to understand the human’s emotion but Artificial Intelligence can understand the same and responds accordingly.

This helps in getting quick answers, making it easy for customers and businesses to resolve their queries. Recently, Google updated the Gmail app with an AI-driven “Smart Reply” feature that predicts responses you may want to send.

The feature uses a machine learning algorithm to analyze your emails and suggest quick, bite-sized responses you may want to send. What it does is, it tries to understand the email message and predicts what preferable responses you can give.

Chatbots powered by AI, interact with the user in a natural way leaving no scope for misunderstanding and enriching the user experience. To be precise, you can create websites or web-enabled devices that can communicate like a human in an effective manner.

• Voice-based search

Voice-based search empowered with Artificial Intelligence is the key driver in enabling seamless voice conversation between users and websites. Siri, Cortana, and Alexa can do virtually anything from assisting you to setting alarms, scheduling meetings, controlling and checking the status of smart-home devices, making and receiving phone calls, getting directions, etc.

As the user interacts with the virtual assistant, they can learn about your voice, intent, preferences and much more as the AI backend code uses a sophisticated algorithm to learn from the data input; predicting the user’s need in a better way. And by doing this, it learns how to work with you better as well as serve you in an enhanced way.

Virtual assistance can be an integral part of all e-commerce web players to enhance web experience and shopping in a smarter way. Digital Assistance also maintains privacy policies, which define how each company uses and shares personal information.

• Machine learning

Machine learning technology – the element of Artificial Intelligence has come along a broader way to provide customization in websites and personalization to the users. It predicts the user’s intention by analyzing previous activities and accordingly adjusts messages based on the interactions and behaviors.

Machine learning makes use of algorithms to make computers learn without being explicitly programmed. Through machine learning the application analyzes data that helps to automate the analytical model building, enabling the systems to find hidden insights. It applies complex mathematical calculations to the big data – over and over, faster and faster to predict future problems and prevent them from occurring.

Some popular scenarios of machine learning used in a web application are:-

• Online recommendation offers from Amazon, eBay, etc
• Spam detection in form submissions
• Search engines

• Boosts the content-quality

It is easily said ‘Trigger the right message to the right buyer’, but doing it is altogether a different challenge. In this digital era, which is the best way to present relevant content to every person who engages with your brand?

The precise answer is, through Artificial Intelligence. It helps the web-marketers to create relevant content for their audience at every phase of the marketing funnel. With the help of algorithms, robust analysis, and scrutinizing the data of competitors, it is easier to gather information about the target audience which includes their likes and preferences.

With such insights, you can curate your content and provide relevant answers to boost customer engagement and rank high on conversions.

• Provides unique store experience

Retailers use Artificial Intelligence to analyze customer data in order to gain insights into customers’ behavior. It provides a personalized approach to each customer by offering shopping recommendations, deals, discounts, and much more.

Chatbots, an extremely multi-faceted tool, is able to detect the tone of the user’s message and determine the best way of responding. AI enables online retailers to boost sales and forecast demand, to gain a better understanding of consumer behavior.

Wrapping it up

Artificial Intelligence is an advanced and decisive technology that adds value to the user experience and organizations. In the coming years, AI will play a crucial role in bringing the web and users close to each other.

Do you want to enhance the user experience of your web application? Get in touch with us and we will get back to you with the most robust solution.

Frequently Asked Questions

1. Why is Artificial intelligence gaining importance?

Artificial intelligence helps automate repetitive learning, add intelligence, adapt progressive learning algorithms, and achieve accuracy.

2. What kind of applications require Artificial Intelligence?

Web and mobile applications that require a better predictive response, voice-based searches, and machine learning can benefit from Artificial Intelligence.

3. What are the famous machine learning cases used in web applications?

Some of the best applications are online recommendations of offers and discounts from Amazon, eBay, etc., Spam detection in form submissions, and Search engines.

4. How can AI offer a personalized experience for customers?

Artificial Intelligence provides a personalized experience by offering shopping recommendations, deals, discounts, and more. Implementing chatbots can detect the user’s tone and determines the best way to respond. AI is becoming the future of web development as it helps boost sales, better understand consumer behavior, and forecast demand.

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