AI/ML

Customers are very demanding today, primarily because of the options available to them. When they reach out to a brand for support, they expect minimal wait times and fast resolution, regardless of the channel they use. Agents, however, have limited bandwidth and can only handle a limited number of cases at a time. So how does one scale customer support? Enter chatbots. But what exactly is a chatbot? That’s a significant question considering the fact that this technology is increasingly becoming a bigger part of our daily lives.  In fact, Gartner research predicts that soon the average person will spend more time interacting with chatbots than with their spouse. With round-the-clock digital support becoming a critical value proposition for brands, more and more businesses are using chatbots to engage with customers to deliver the SLA expected of them. At a technical level, a chatbot (derived from “chat robot”) is a piece of code (program) that simulates human interaction through text or voice communication. Today, chatbots can be customised and used in multiple ways such as: Chatbots that interact through smart speakers Chatbots deployed on smart home devices Chatbots that can be deployed on popular messaging platforms and web In addition to having a conversation with a service agent, customers can now interact with an intelligent software that helps them to find answers fast. Whether through text or voice, chatbots can communicate with customers and respond to requests faster. To put it simply, Chatbots are Artificial Intelligence (AI) powered digital assistants that answer common customer questions. They help customers quickly resolve simple and routine issues freeing up agent time to work on more complex issues that require human interactions. How does one create customer service chatbots Customer service chatbots resolve simple, repetitive tasks that don’t require interacting with a human customer service agent. For instance, if a customer wants to know how to reset a password or the estimated delivery time for a product they ordered, a customer service chatbot quickly accesses the relevant information and answers the question without keeping the customer waiting at the other end. And while this is happening, your service agents can focus on resolving more complex customer issues and build stronger customer relationships. If you are looking to invest in chatbot technology, your first goal is to establish the most common customer requests to identify what to automate. We suggest the following six tips that you should keep in mind when designing your first AI-powered chatbot: 1. Personalise every greeting Customer service agents are trained to be warm, greet customers by name, and recognise their service privilege status. A chatbot can do the same thing in the background, powered by AI. Chatbots can be programmed to retrieve their name to ensure chatbots greet them like a human agent would. 2. Move from static to conversational Customers hate the idea of filling out an online form and then having to wait for 12 -24 hours for a response. An AI powered service chatbot can dynamically ask a series of relevant questions based on customer inputs and make the interaction more engaging. It also helps resolve the customer issues faster. And in the case where agent intervention is necessary, they will already have all the relevant information logged by the chatbot available in their panel. 3. Create interactive FAQs Traditionally, customers are prompted to visit the FAQ section of a website or app to resolve issues in a self-service mode. Chatbots turn this process around its head. They bring the FAQ answers to customers. You can stack your common FAQs and their answers into your AI interface, including all related questions and their answers.  And with natural language processing (NLP) capability built into the AI engine, chatbots recognise everyday use language and respond to customer queries. Now your customers can find what they are looking for faster than before. 4. Deploy chatbots to additional channels Businesses today deploy customer service across multiple digital channels such as web, messaging, and social allowing customers to connect with your brand in the way they want.  Salesforce research indicates that an average customer today uses nine different channels to interact with brands. This variety of options creates multiple opportunities to deliver 360-degree customer service to meet their ever-changing behaviour. You can dive deeper into your analytics to identify the channel that gets the maximum traffic for your brand, and then identify the top customer service requests on that channel. Automate your chatbot to respond to these requests and save time for your agents. 5. Engage customers with formatted text and content Basic text is all right for answering simple questions, but professionally formatted text using a range of font styles, sizes and colors enhances the customer experience. You can even insert images and interactive menus into the chat. And because it is powered by AI, your chatbot can surface a product menu, a list of articles, or customer support options, based on wat the customer asked, all within the chat. 6. Embed process automation in chatbots With AI, you can empower customers to self-serve themselves by assisting them with guided, step-by-step instruction right within the chat console. Work with your teams to identify tasks that are easy for customers to complete on their own. Therese are typically tasks that can be easily automated without needing any human intervention like renewing an insurance policy. Once your team has identified these simple and common use tasks, you can program your chatbot to guide customers throughout the service journey. And for more complex issues, when the chatbot has to hand over the conversation to an agent, the agent is already empowered with all relevant information about the case so they can resolve it quickly. Scale customer service with chatbots Your customers will recognize the value your customer service chatbots bring to them with quick, efficient resolutions to their requests and concerns. And you agents will have more tie to focus on complex customer service requests instead of answering FAQs. With AI powered chatbots, you can easily scale support to handle any case overload as and when they come your way. AI-powered chatbot technology holds the promise to reinvent the customer experience. And high-performing service teams are leading the AI powered chatbot revolution to augment their existing human customer support teams. In today’s digital first context, where speed of service is king, chatbots are helping companies stay ahead of the curve. As a Gold Salesforce Consultant, Girikon can help you deploy AI powered chatbot based customer service at scale. Contact one of our experts to learn more.

Artificial intelligence (AI) is growing in stature in the marketing realm.  Marketers today rank AI as their #1 priority for investment, according to a recent State of Marketing Report published by Salesforce. The adoption of AI is staggering with about 84% of marketers reporting that they use AI in their customer acquisition and retention engines, that’s almost 300% growth in 2 years. What exactly are marketers doing with AI? The usage of AI is crossing all barriers, from improved segmentation and personalisation, deeper insight, forecasting and process automation. With the growth in advertising technology, riding on big-data driven AI, advertising companies reimagined the process of delivering digital ads. According to eMarketer,  online ad sales rose from $60 billion in 2019 to an estimated $97 billion in 2022. What do sales teams feel about the impact of AI on what they do? They would tell you-cautiously optimistic. According to latest research from Salesforce, 86% of sales reps view AI as having a positive impact on their future roles. However, 68% of the same reps had concerns as well. And as you would imagine, mostly concerning the very relevance of their existing jobs. 31% percent said technology might eventually negatively impact the art of selling, as AI driven optimisation of sales interactions replaces human-to-human relationships. Five reasons to be happy about AI in advertising While it is only natural to fear the unknown, there is reason to believe that AI will make the existing jobs of sales reps better. AI augments the sales process and leaves people to do what they do best which is to be human. AI has the potential to level the playing field for both advertisers and publishers. It empowers sales teams, who don’t have the budgets that large corporations or media companies may have, with the tools they would like. AI-driven ad technology can help sales teams boost both the effectiveness of their messaging and their efficiency. 1. Improved data unity and synchronization With the impending censure of third-party cookies in Apple and Chrome’s privacy policies, marketing professionals are increasingly seeking first-party data to power their marketing initiatives. Merkle’s 2021 Customer Engagement Report stated that first-party data was a strategic priority for a staggering 88% of the marketers. For publishers, first-party data is vital to building advertiser centric audiences; and for advertisers, first-party data is increasingly vital for targeted advertising. And that’s where the challenge is. First-party data is not only complex, more often than not, it is fragmented and ill organised. According to Salesforce research, 64% of customers start their purchase process on one device and finish on another. With the proliferation of smartphones and devices, marketers have to deal with an average of 12 primary sources of customer data, an increase of 20% from 2020. And as one would expect, most of the data residing in these sources has inconsistent identity information, expired or outdated information, and unconventional taxonomy. AI can be a great asset to a common Customer Data Platform to significantly improve identity matching. Algorithms can execute “fuzzy logic” on IDs and resolve or isolate discrepancies. AI can also ensure data consistency by mapping data from siloed systems to a common data model. 2. Better audience segmentation and discovery Customers today prefer digital first, and they want relevant experiences. They want the messaging to be useful and timely in their digital lives. Delivering a personalised experience mandates the need for organised data as well as smart algorithms to discover customer segments and reveal their needs that may be impossible to do manually in real-time. AI stands out at intelligent segmentation, discovering groups of customers and prospects with common attributes at a scale that is impossible for a human analyst to achieve. AI algos can sift through billions of records of customer data to identify meaningful patterns and segment audiences intelligently for more effective and targeted marketing programs. 3. Natural language shakes hands with technology Natural-language processing (NLP) and image recognition are 2 areas which are extremely promising when it comes to AI. Chatbots have taken customer service to the next level with conversational customer service. Voice assistants such as Siri and Alexa are already changing the user experience whether we want to book a ticket or are in the mood to have Chinese. As voice recognition technology approaches 95%+ accuracy, voice navigation will become an intrinsic part of customer engagement. Voice navigation is already built into call centre systems and used in analytics as well to some extent. However, AI based marketing technology is at the cusp of something never seen before. Imagine an ad sales rep never having to use a keypad again to make the right selection. With less and less human clicks efficiency will improve significantly, and so will the efficacy of the whole process. And this will lead to ad reps spending less time in searching and updating and have more time to do what they do best – selling. 4. Back-end processes efficiency We’ve already talked about how AI can help unify and harmonize data. It can also help sales teams become more efficient by prioritising their efforts, and allowing them to focus their time on key tasks while automating the mundane ones. Lead Scoring was one of the first areas to benefit from AI technology. While there was resistance from sales teams initially in adopting the technology, and rightly so, with the uncertainty of the what the unknown will do to their current jobs. Questions such as – How can a software qualify a good lead better than me, were commonplace. But with technology integration into existing systems, many sales reps now use algorithms regularly to augment their priority their prospects. The same is true for determining next best actions based on interaction history and scheduling meetings. AI can help ad sales teams match available inventory with “most likely to close” sales opportunities thereby significantly trimming the time spent on low-probability leads. 5. Improved measurement and optimisation In the post pandemic period, digital ad spend has seen a meteoric rise. Brands now spend more than half of their advertising budgets on digital platforms. AI can help aggregate and analyse all that data seamlessly to help advertisers establish the impact of campaigns viz a viz desired outcomes, such as sales. Simply put, AI can help ad sales teams separate the noise and identify what works. To measure the efficacy of multi-channel campaigns, one needs to harvest information from dozens of sources. And that’s not all. They also need to apply complex models to determine which aspects of the campaign, channels, devices, and tactics made an impact. AI-driven tools can help ad sales teams with automated recommendations to optimize campaigns based on the historical performance of ads. AI excels at automating tedious tasks, and is also fantastic at sifting through huge amounts of data at unimaginable speed. Something humans can’t do. By allowing AI technology to do what it does best, we make the entire process of ad sales less artificial and more intelligent. Girikon is a Gold Salesforce Consultant delivering value to customers across the globe for over a decade. Contact one of our experts to know how AI can help unlock the true value of your advertising sales.

PyTorch is an open-source deep learning framework that offers flexibility and speed, enabling developers and researchers to design, train, and deploy AI models with ease. It is backed by the Linux Foundation and supported by AWS, Meta Platforms, Microsoft Corp, and Nvidia. PyTorch has become the go-to framework for cutting-edge AI research and enterprise-scale applications. Its popularity stems from a Pythonic, intuitive design, dynamic computation graphs that make experimentation seamless, and strong ecosystem support. What sets PyTorch apart from other popular neural network-based deep learning frameworks, such as TensorFlow, is that it uses static computation graphs. On the contrary, PyTorch uses dynamic computation graphs, allowing greater flexibility when building complex architectures. So, let’s dive deep into the nitty-gritty of PyTorch, understand its features, benefits, and major applications, where it enables faster experimentation and prototyping. What is PyTorch? PyTorch is an open-source deep learning framework that supports Python, C++, and Java. It is often used for building machine learning models for computer vision, Natural Language Processing (NLP), and other neural network tasks. It was developed by Facebook AI Research (now Meta), but since 2022, it has been under the stewardship of the PyTorch Foundation (part of the Linux Foundation). Despite being a relatively young deep learning framework, PyTorch has become a developer favourite for its ease of use, dynamic computational graph, and efficient memory usage. Understanding How PyTorch Works Two core components of PyTorch are tensors and graphs. Let’s understand them: Tensors Tensors are an essential PyTorch data type that are similar to multidimensional arrays. They are used to store and manipulate a model’s inputs, outputs, and other parameters. In addition, tensors resemble NumPy’s ndarrays, but unlike them, tensors can run on GPUs to accelerate computing for large workloads. Graphs Graphs are data structures consisting of connected nodes (vertices) and edges that help the framework track computations and calculate gradients during training. With two processes: forward propagation, where the neural network carries the input and delivers a confidence score to the nodes in the next layer, until the output layer, where the ‘error’ of the score is calculated. Whereas, in the backpropagation process, gradients of the loss function are computed and sent backward to update parameters, PyTorch keeps a record of tensors and executed operations in a directed acyclic graph (DAG) consisting of Function objects. In this DAG, the leaves are the input tensors, and the roots are the output tensors. Unlike static frameworks like TensorFlow, PyTorch builds its graph at runtime (i.e., dynamically as the code runs). This makes the framework more flexible, easier to debug, and an ideal choice for accelerating innovation while supporting enterprise-grade deployment. PyTorch can use debugging tools of Python. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE from Python, for debugging. Why Do We Need PyTorch? PyTorch works very well with Python, and uses its core concepts like classes, structures, and loops, and is therefore more intuitive to understand. The PyTorch framework is seen as the future of deep learning. There are many deep learning frameworks available to developers today, with TensorFlow and PyTorch among the most preferred. PyTorch, however, offers more flexibility and computing power. For machine learning and AI developers, PyTorch is easier to learn and work with. 5 Key Features of PyTorch Here are some capabilities that make PyTorch suitable for research, prototyping, and dynamic projects. 1. Easy to Learn PyTorch follows a traditional programming structure, making it more accessible to developers and enthusiasts. It has been well documented, and the developer community is continuously improving the documentation and support. This makes it easy for programmers and non-programmers alike to learn. 2. Developer Productivity It works seamlessly with Python, and with many powerful APIs, can be easily deployed on Windows or Linux. Most PyTorch tasks can be automated. Which means with just some basic programming skills, developers can easily boost their productivity. 3. Easy to Debug PyTorch can use Python’s debugging tools. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE for Python, for debugging. 4. Data Parallelism PyTorch can assign computational tasks amongst multiple CPUs or GPUs. This is made possible by its data-parallelism feature, which wraps any module and enables parallel processing. 5. Useful Libraries PyTorch is supported by a large community of developers and researchers who have built tools and libraries to expand PyTorch’s accessibility. This developer community actively contributes to developing computer vision, reinforcement learning, and NLP for research and production. GPyTorch, BoTorch, and AllenNLP are some of the libraries used with PyTorch. This provides access to a robust set of APIs that further extend the PyTorch framework. Top Benefits of PyTorch Python-friendly: PyTorch was created with Python in mind (hence the prefix), unlike other deep learning frameworks that were ported to Python. PyTorch provides a hybrid front end that enables programmers to easily move most of the code from research to prototyping to production. Optimized for GPUs: PyTorch is optimized for GPUs to accelerate training cycles. PyTorch is supported by the largest cloud service providers, including AWS, which currently supports the latest version. AWS includes its Deep Learning AMI (Amazon Machine Image), which is optimized for GPUs. Microsoft also plans to support PyTorch on Azure, its cloud platform. PyTorch includes built-in data parallelism, enabling developers to leverage multiple GPUs on leading cloud platforms. Plethora of tools and libraries: PyTorch comes with a rich ecosystem of tools and libraries that extend its capabilities and availability. For instance, Torchvision, PyTorch’s built-in set of tools, allows developers to work on large and complex image datasets. Large Network & Community Support: The PyTorch community, comprising researchers across academia and industry, programmers, and ML developers, has created a rich ecosystem of tools, models, and libraries to extend PyTorch. The objective of this community is to support programmers, engineers, and data scientists to further the application of deep learning with PyTorch. Production & Deployment Challenges with PyTorch Scaling Models Efficiently: Sometimes, scaling applications at production requires optimization to support a large user base. System Integration: PyTorch models don’t usually integrate with the overall enterprise workflow, so APIs or conversion tools are frequently required. Performance Optimization: To reduce latency and boost throughput in real-time applications requires tuning and, in some instances, specialized hardware. Lifecycle Management: Model drift may occur, so PyTorch models need monitoring, retraining, and version control to ensure reliability. 5 Ways in Which AI Apps Can Use PyTorch With PyTorch, engineering teams can build deep learning predictive algorithms from datasets. For instance, developers can leverage historical housing data to predict future housing prices or use a manufacturing unit’s past production data to predict the success rates of new parts. Other common uses of PyTorch include: Image Classification PyTorch can be used to build complex neural network architectures, such as Convolutional Neural Networks (CNNs). These multilayer CNNs are fed thousands of images of a specific object, say, a tree, and, much like how our brains work, once trained on a data set of tree images, they can identify a new picture of a tree they have never seen before. This application can be particularly useful in healthcare for detecting illnesses or spotting patterns much faster than the human eye can. Recently, a CNN was used in a study to detect skin cancer. Handwriting Recognition PyTorch makes it easier to build systems that can recognize human writing across people and regions. These systems can even account for inconsistencies in human handwriting across people and languages by leveraging flexible tools for image processing and neural networks. Developers can train these models to learn and understand the shapes and strokes of handwritten characters, enabling apps to convert notes or forms into digital text automatically. It is a useful capability for digitized entries, smart pens, or educational tools. Forecast Time Series Another type of neural network is a Recurrent Neural Network (RNN). They are designed for sequence modelling and are particularly useful for training an algorithm on past data. It can make predictions based on historical data, allowing it to make decisions based on the past. For instance, an airline operator can forecast the number of passengers it will have in 3 months, based on data from previous months. Text Generation RNNs and PyTorch are also used to generate human-like text, from simple word structures to advanced chatbot responses. In text generation, by training RNNS and AI models on large datasets, developers can build AI applications that produce content, answer questions, or provide customized communication. For instance, AI assistants such as Siri, Google Assistant, and other AI-powered chatbots rely on similar systems to enhance and personalize user interaction. Style Transfer One of the most exciting and popular applications of PyTorch. It uses a set of deep learning algorithms to manipulate images and transfer their visual styles onto other images, creating new images that combine the data of one with the style of another. For example, you can use your vacation album images, apply a style-transfer app, and make them look like paintings by a famous artist. And as you would expect, it can do the reverse as well. Convert paintings into contemporary photos. Closing Statement Undoubtedly, PyTorch has made deep learning more accessible than ever before. Since its architecture is uniquely suited to support both rapid research experimentation and the scalability required for production development, it’s becoming a trusted framework in the research and development industry. Therefore, its growing influence reflects how advances built on PyTorch are moving beyond research to shape enterprise platforms. Salesforce, the world’s leading CRM platform, is one such example. The CRM platform embeds trusted AI across all its product offerings, from predictive analytics and SMS apps to Voice Agents that enhance customer engagement. As a Gold Salesforce Partner, Girikon helps organizations leverage these advances in Salesforce CRM and bring AI innovation and tangible business outcomes. Get in touch today to know how we can help enterprises embed trusted AI into CRM workflows and transform how you interact with customers.

TensorFlow is an open-source library created by the Google Brain team to build enterprise-grade machine learning algorithms. TensorFlow bundles together a host of machine learning models and algorithms and with the use of common programming frameworks, makes them useful. TensorFlow uses Python and JavaScript to build user friendly APIs for connecting with apps, and uses core C++ to execute the app functionalities. While it is still early days for machine learning technology, it continues to evolve rapidly, introducing us to a new world of advanced algorithms and deep learning. Deep learning uses algorithms commonly referred to as Neural Networks. As the name suggests, they draw inspiration from our biological nervous systems, led by the brain, to process information. Deep learning algorithms enable computing devices to identify every single bit of data, establish what it means and learn patterns. TensorFlow is a tool to develop deep learning models. It is an open-source AI library that uses data flow graphs to build learning models. With TensorFlow, programmers can build large-scale, multi-layered neural networks. TensorFlow is primarily used to perceive, understand, classify data and create predictive models. Main Use Cases of TensorFlow While TensorFlow can be used for many applications, here are 5 commonly used applications in the world of artificial intelligence. Voice/Sound Recognition One of the most popular use cases of TensorFlow is audio signal based applications. When fed appropriate data, neural networks can perceive and understand audio signals. These can be: Voice recognition — primarily used in Internet of Things (IoT) applications, Automotive applications (Voice command based actions), Security (Authentication) Voice search — Commonly used in Telecom and by mobile phone manufacturers Sentiment Analysis — used in CRM applications Flaw Detection (noise analysis) —Automotive and Aviation applications The world is familiar with the common use case of voice-search and voice-activated assistants. This use case has been widely popularised by smartphone manufacturers and Mobile OS developers such as Apple’s Siri, Google Assistant and Microsoft Cortana. Understanding and analyzing language is another widely used use case for Voice Recognition. Speech-to-text applications are used to extract and understand sound bites in larger audio files, and convert it into text. CRM is another area were voice/sound based applications can be implemented to deliver a better and smarter customer service experience. Imagine a scenario where TensorFlow algorithms fill in for customer service reps, and guide customers to the right set of information much faster than an agent. Text Based Applications This is another commonly used application of TensorFlow. Text based applications for instance sentiment analysis can be used in CRM apps and Social Media for improving the customer or prospect experience, Threat Detection, used in Social Media and Government applications and Fraud Detection, used by Insurance, Finance companies are some common examples. Language Detection is another popular use case of TensorFlow for text based applications. We are quite familiar with Google Translate. More than 130 languages can be translated into each other using this service. An AI powered version of a translate engine can be used in common real world situations like translating heath diagnosis technical terminology or legal jargon in contracts into plain language. Text Summarization Google also came up with sequence-to-sequence learning, a technique for shorter text summarization. This can then be used to build headlines for news and articles. Another use case for TensorFlow popularised by Google is SmartReply. It automatically generates e-mail responses based on text recognition and contextual understanding. Image Recognition This use case is primarily used by Social Media and Smartphone Manufacturers. Facial Recognition, Image Search, Motion Detection, Computer Vision and Image Clustering are nowadays being deployed in Warehousing, Healthcare, Automotive, and Aviation Industries. Google Lens is another example where Image Recognition is being used to understand the content and context to help identify people and objects within images. TensorFlow object recognition algorithms have the ability to identify and classify random objects within larger images. This has found use in engineering modelling applications such as creating 3D spaces from 2D images. Facebook’s Deep face is another example of photo tagging using image recognition technology. Deep learning technology can identify an object in an image never seen before by analyzing thousands of images with similar objects. Healthcare Industry is also at the cusp of using Image Recognition for faster diagnosis.  TensorFlow algorithms can process information and recognize patterns much faster than the human eye to spot illnesses and detect health problems faster than ever. Time Series TensorFlow Time Series algorithms is another method used today to establish patterns and forecasting of time series data. Meaningful statistics can be derived by these algos along with recommended actions. TensorFlow Time Series algorithms allow forecasting of generic time periods apart from generating alternative versions of the predicted time series. A popular use case for Time Series algorithms is Recommendations. Time Series Recommendations has seen widespread usage amongst leading organizations such as Netflix, Google, Amazon, where they analyze and compare activity of millions of users to determine what a customer might wish to view or purchase. And with every interaction, while recording the activity of every action, these recommendations get even smarter. For instance, they throw up content what your family members or friends like or offer you a gift they might like. Finance, Insurance, Government, Security and Threat detection, Predictive Analysis, Resource Planning and forecasting are some of the other use case scenarios of TensorFlow Time Series algorithms. Video Detection TensorFlow deep learning algorithms can also be used on video data. This is used in Motion Detection in Automotive and Aviation, Role based Gaming, Security and Threat Detection. Today, universities are doing deep research on Video Classification at a large scale to perceive, analyze, understand, classify video data. NASA is using TensorFlow algorithms to build a system for orbit classification and object clustering of asteroids. Consequently, they will be able to classify and predict near earth objects. TensorFlow is an open-source framework, allowing developers the freedom to work on innovative and disruptive use cases, which will contribute further to Machine Learning technology. Amongst many things, TensorFlow’s popularity is primarily due to the computational graph concept, automatic differentiation, and the adaptability of its python API structure. This makes TensorFlow more accessible to developers to solve real problems. Here are some advantages of TensorFlow. 1. Scalable The TensorFlow library is well defined and structured. This means it works just as efficiently on a mobile device as on a powerful computer. 2. Open Source The TensorFlow library is available free of cost. Anyone, anywhere can work on it and use it to solve problems. 3. Graphs Tensorflow has a very powerful, inbuilt data visualization capability. This makes it easier for developers to work on neural networks. 4. Debugging Tensorboard, which is a part of TensorFlow, allows easy debugging of code blocks. This reduces the need for combing through the whole code. 5. Parallelism TensorFlow uses Central Processing Unit (CPU) and Graphics Processing Unit (GPU) for its functioning. Developers can use the architecture freely based on the problem they are trying to solve. A system uses GPU by default, which is why  TensorFlow is sometimes referred to as a hardware acceleration library because it reduces memory usage. 6. Compatible TensorFlow is compatible with popular programming languages like Python, C++, JavaScript, etc. This allows developers the freedom to work in an environment they are most comfortable with. 7. Architectural Support The TensorFlow architecture uses Tensor Processing Unit (TPU). This makes computation faster than what one would get when using CPU and GPU. TPU models can be easily deployed on the cloud and work faster than CPU and GPU. 8. Library management With the Google backing, TensorFlow is updated regularly with enhanced capability and flexibility with every release. AI is already an intrinsic part of Salesforce, the world’s leading CRM platform. As a Gold Salesforce Partner, Girikon is the preferred choice for many Salesforce customers across the globe. To know more about how AI can work for your business, contact us today.