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TensorFlow is an open-source library created by the Google Brain team to build enterprise-grade machine learning algoriths. 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.

What is generative CRM? Generative CRM combines the power of generative AI with CRM data to boost productivity and efficiency of teams. It has the power to execute limitless functions such as responding to queries, generating conversational text, suggesting next steps, drafting emails and more. The beauty of Generative AI is that the more people use it, the smarter and faster it will become. In the coming months and years, Generative CRM will effortlessly perform tedious everyday tasks, freeing up time of your teams so they can focus on more important tasks. With the ability to comb the internet for relevant data in a matter of seconds, it can help draft more meaningful responses thereby significantly boosting the efficiency of teams. How generative CRM can boost productivity, efficiency, and customer relationships People spend hours executing ordinary day to day tasks. They sift through data and information, wrack their brains to come up with new social media ad campaigns, iterate multiple times to create a perfect email pitch for a prospective customer, and engage in a fire fight to resolve issues of dissatisfied customers. What if they had a tool to streamline all of that, irrespective of the industry or department they work in?   Generative AI is on the brink of redefining CRM across companies in the coming years. Let us dive deeper to understand how this new age tech, when combined with your CRM, can help teams become more productive and deliver stunning customer experiences. The employee view If you are a new sales rep, and you have just been assigned a new account, it would take you many hours, perhaps even days to get an overview of the company, catch up on the latest company activities, discover the right contacts, and prepare an introductory email. With Generative AI, all this can be done in a matter of seconds by your CRM. So you can refine that email and connect with the right person sooner than ever. This is the potential of generative CRM. When the power of generative AI combines with your CRM data, it unlocks a never seen before power of your CRM. The view across teams Generative AI is poised to reshape how teams work across departments in the years to come. It will empower enterprises to quickly and effortlessly generate AI-driven content across multiple departments -sales, customer service, marketing, commerce, and IT. Service teams would have the power to create automated, smarter, more personalized chatbots that can engage with customers just the way a human rep would, but much faster.  They would have the ability to anticipate, comprehend and respond to customer requests faster than ever. For marketers, generative CRM can help in quickly creating accurate, compelling product descriptions that are optimized for web search. Here are some key benefits that generative CRM would deliver going forward. Reduce time to value AI has already been around for a while with Salesforce Einstein delivering over 200 billion predictions every day. Today, AI products like ChatGPT and Dall-E are empowering millions of people across industries to work more effectively. Generative AI is a deep tech that will filter out the noise that we encounter on the web. If you can ask the right questions contextually, generative CRM will be smart enough to know what to look for and how to present it to you. Free up humans for high-value work If you are a sales rep, imagine trying to acquire a potentially big new customer. You will have to spend hours sifting through data to strengthen your sales pitch, and by the time you do so, it may end up being archaic. You then comb your network and the prospect website and social media handles to find that perfect person to connect with, only to find that they moved on to another company recently. These repetitive, cyclical and routine tasks to acquire a new customer often waste precious time.  Generative AI can speed up these routine activities to make you far more productive. It will allow you to spend more time to do the real thing, which is building relationships with prospects and customers. AI that you can trust Security and privacy will be a critical aspect of generative CRM. Governed by guidelines that specifically address security and privacy concerns, generative AI will build on long standing principles for trusted AI. While publicly available generative AI tools depend only on publicly available data and information, generative CRM will be grounded on private and secure customer data, while also drawing on publicly available data and information such as social media and corporate websites. The ability to fuse public and private data is what makes generative AI driven CRM a trusted, and impactful experience for customers. Generative AI at Salesforce AI is already an integral part of the Salesforce Customer 360 platform, and its potential is limitless. Salesforce Einstein AI technology delivers over 200 billion predictions on a daily basis across multiple Salesforce’s business apps. This includes: Sales, which utilises AI powered insights, to establish the best next steps so reps can close deals faster. Service, which utilises AI to have bot-based natural conversations and provide the best fit answers, freeing up reps to work on more complex and important tasks. Marketing, which uses AI to better understand customer behaviour and personalize marketing campaigns to boost their efficacy.   Commerce, which utilises AI to deliver personalized buying experiences and smarter ecommerce. With generative AI, businesses can connect with their audiences in completely new, more engaging ways across every interaction. Guidelines for Trusted Generative AI Like they do with all their technology innovations, Salesforce is rooting ethical guidelines across all their products to assist businesses innovate rapidly and responsibly.  With the tremendous potential and challenges emerging in generative AI, Salesforce is building further on their Trusted AI Principles with a new set of guidelines to push for responsible development and deployment of generative AI. Here are 5 such guidelines. Accuracy: Use models to deliver verifiable results allowing customers to train models on their own data. Communicate when authenticity of the AI’s response cannot be established with certainty and enable users to ratify these responses. This can be achieved by citing sources, explaining why the AI gave those responses, underscoring areas to double-check such as stats, dates, and creating checks and balances that prevent certain tasks from being fully automated (like code review before deployment) Safety: Effort should be made to mitigate any bias or harmful output by conducting robustness assessments. The privacy of any personal private information should also be protected by creating guardrails. Honesty: When aggregating data to train and evaluate AI models, the source of data should be respected by ensuring their consent for use. Transparency in communication should be maintained by clearly stating that autonomously generated AI content has been delivered. Empowerment: While in some cases, a fully automated AI driven process may be the best option especially for non-critical, publicly available data, there are cases where AI should augment a human role, especially where human judgment is necessary. One needs to establish the right balance to turbo charge human capabilities and make generative AI solutions accessible to all. Sustainability: In our endeavour to establish more and more accuracy in our models, we should develop most appropriate-sized models wherever possible to reduce our carbon footprint. Summary If you are a Salesforce Consultant, this is an exciting time for you. Generative AI has the power to take CRM to the next level. By following the above guidelines, you can deliver never before seen value to your customers with the power of AI. Girikon is a Certified Salesforce Development Partner delivering value to customers across the globe. To know more about how Generative CRM can work for you, contact us today.

In March 2023, Salesforce launched Einstein GPT, the World’s First Generative AI for CRM. Einstein GPT uses the power of generative AI to deliver personalized content across every Salesforce product, thereby making teams more productive and delivering a better customer experience. Einstein GPT is open and scalable, and supports public and private AI models built specifically for CRM. Einstein GPT is trained on trusted, real-time data and seamlessly integrates the with the OpenAI framework to deliver out-of-the-box generative AI capabilities to Salesforce users. The new ChatGPT app for Slack integrates seamlessly with OpenAI’s deep AI technology to power instant conversations, and provide research and writing assistance. Whether it be sales, service, marketing or commerce, Einstein GPT for Salesforce will transform every customer experience at never seen before scale. In a sense, it opens a new door to the AI future for all Salesforce customers. Salesforce has fused its proprietary AI models with generative AI technology in Einstein GPT that unifies and synchronises all of a company’s customer data. Using Einstein GPT, customers can now easily connect that data to OpenAI’s advanced AI models, or use their own model and natural-language prompts directly within their Salesforce CRM to seamlessly generate content that self-adapts continuously to changing customer needs in real time. For instance, Einstein GPT can create personalized email drafts for sales reps, generate automated and tailored responses for service reps to respond to customer queries more quickly, generate appropriate content for marketers to augment campaign efficacy, and developers can get access to auto-generated code allowing them to build and deploy apps much faster. Going Deeper: Einstein GPT in CRM Salesforce Einstein is already delivering over than 200 billion AI-driven predictions everyday.  Einstein GPT is the next generation of Einstein, and by combining proprietary Salesforce AI models with OpenAI, customers can use natural-language prompts on CRM data to trigger powerful, time-saving automations, and create personalized, AI-generated content. Sales: Auto-generate routine tasks like drafting emails, preparing meeting schedules, and prepping for follow ups. Service: Auto generate articles from case note archives. Auto-generate conversational and personalised chat to smartly engage with customers. Fast track service interactions and enhance the customer experience. Marketing: Dynamically generate personalized and engaging content faster than ever to interact with potential and existing customers across channels. Slack : Get AI-driven customer insights in Slack for eg. smart summaries of sales opportunities and self updating knowledge articles. Developers: Developers can auto generate code by utilising Salesforce’s proprietary language model to ask contextual questions for languages like Apex through an AI chat assistant. Einstein GPT is in-built in Salesforce. Which means you can use your private data to tailor everything it generates suited to your unique business. And since Einstein GPT is available across the entire Salesforce platform, it can improve every single customer experience. Salesforce understands that generative AI encompasses more than just ChatGPT. Einstein GPT  has been designed to allow seamless integrations with other language models. This allows developers to bring their preferred model using normalized APIs and an open network of AI partners. Einstein GPT is designed to empower businesses with path breaking AI capabilities, using your own data and models to drive customer experiences. Embedding AI into the Salesforce platform has delivered huge operational efficiencies for partners and customers.  Generative AI technology has the potential to transform the way companies engage with their customers, deliver powerful experiences, and drive customer retention. Generative AI technology will drive the next generation of customer experience. Einstein GPT for Salesforce Developers As technology innovation progresses, so does the way developers write and analyze code. Generative Artificial Intelligence is perhaps the most exciting development in recent years for code generation and analysis.  This technology has the power to make development faster, more efficient and accurate. Let’s look at how Salesforce AI Research is powering Einstein GPT for developers across the globe and how it will change the way apps are deployed on Salesforce. Generative AI for code (Apex) Code generation involves using machine learning algorithms to analyse large amounts of existing code, and then generate new code based on that analysis. This is particularly useful for tedious tasks, such as drafting emails. One obvious and huge benefit of code generation is that it saves a lot of time for developers. Instead of writing every line of code from ground up, developers can use AI-powered tools to generate most of the required code automatically. Not only does this accelerate the entire development process, it also reduces the chances of human error. Code generation has many benefits, including: Code standardization: Automating generation of repetitive code blocks that guarantees consistency and standardization of code. Accelerated prototyping: Generative AI based code generation accelerates the prototyping process by quickly creating run of the mill code. Codebase becomes more scalable because of standardization. Simplified code: Generative AI automates the creation of repetitive code blocks thereby simplifying it. Code becomes easier to maintain and scale. Salesforce Consultants and Developers can now derive the benefits of Einstein GPT within the IDE experience. With inbuilt natural language processing capability, developers can have auto generated code created for them within the Code Builder as per their specific needs. The machine learning algos that drive the generative AI experience in Einstein GPT are based on Salesforce’s proprietary models and enhanced with best-in-class coding guidelines. Static and dynamic Apex analysis Code analysis is another field where AI is making significant progress. As software development become more and more complex, it becomes increasingly challenging for developers to precisely analyse and understand the code. Salesforce is piloting a new capability this year for Apex analysis. With this feature, developers can quickly and precisely analyse large amounts of Apex code, identify potential defects, runtime and other inefficiencies. This will save Salesforce Partners and developers a substantial amount of time and effort. They would no longer have to manually sift through each line of code to find potential problems. One of the key benefits of AI is that it can identify potential problems easily that developers may miss at runtime. AI-driven code analysis and code generation work synchronously. Using AI powered static and dynamic analysis, patterns in your code base will be fed back into the code generation process in run time, and vice versa. Conclusion AI-driven code generation and analysis is changing the entire development paradigm. And this is just the beginning. Going forward, Salesforce has plans to assist with automated test generation, intelligent code clarification, and more. If you are a Salesforce Consultant, this is an exciting time for you. Generative AI has the power to take CRM to the next level. With Einstein GPT you can get multi-dimensional insights of your CRM data and deliver never before seen value to your customers with the power of AI. Girikon is a Certified Salesforce Development Partner delivering value to customers across the globe. To know more about how Einstein GPT can work for you, contact us today.

In today’s competitive era, businesses must consider integrating and synchronizing products, their pricing, inventory, and other business details to fulfill the evolving needs of modern consumers. To meet these demands, businesses require robust tools such as Salesforce Commerce Cloud and MuleSoft. You can seek Salesforce support from a reliable service provider to know more about these platforms. The integration of MuleSoft and Salesforce Commerce Cloud ensures a connected commerce experience and allows organizations to unify commerce data from any system, and quickly launch into new markets while providing enterprise-grade security to ensure the safety and security of corporate data. In other words, MuleSoft and Salesforce Commerce Cloud can provide a connected commerce experience. What is Salesforce Commerce Cloud? Salesforce Commerce Cloud is an e-commerce solution that empowers organizations to sell their products and services across different channels, including social, mobile, and the web. This cloud platform helps retailers provide a superior shopping experience to their customers.     This platform provides brands the agility to remain competitive and ensure the following: Proficiently launch and manage stores across different channels, geographies, and dialects through a cloud-based commerce platform. Test and deliver changes. Modernize while prioritizing actionable insights that drive better business decisions. Salesforce Commerce Cloud Is Only as Powerful as the Data That Lies Behind It With Salesforce Commerce Cloud, businesses can take the online shopping experience a notch higher than that of a traditional e-commerce platform by capturing customer data. In fact, this innovative platform has transformed the way organizations conduct their business by launching into new markets faster while ensuring security from threats. This is possible through API-led connectivity that amalgamates crucial data from legacy systems, CRM, ERP, and partners like delivery logistics providers. However, digital transformation via API-led connectivity depends significantly on the data that backs it. This is where Salesforce Commerce backed up by MuleSoft comes into the picture as it connects all the commerce systems and creates a steady customer view across the organization. MuleSoft’s Anypoint Platform allows you to centralize data while creating an omnichannel experience. Since customer data is an asset that can be leveraged by organizations to improve customer service by using digital technologies and integrating data while making it accessible in real-time. Real-time data ensures quicker customer support and increased satisfaction. APIs help in integrating different systems together and creating a data-driven model. This ensures precise decision-making. In addition, such an approach to data provides insights by tracking customer engagement patterns and gathering more data. By integrating customer data with Salesforce Commerce Cloud, you can get a comprehensive view of your customers while furthering your brand loyalty by having all customer data gathered in a single solution. What is MuleSoft? MuleSoft is a reputed provider of Enterprise Integration Platforms. MuleSoft connects all the data, applications, and devices across different business entities. They enable organizations to gather their data in real-time, actionable, and secure information. MuleSoft along with its Anypoint Platform is one of the best integration platforms available and allows businesses to connect with different applications while ensuring system-to-system integrations are easy and cost-effective. Additionally, the platform provides a unified platform to help organizations manage their business process activities to enable efficient connectivity across people, devices, and systems. MuleSoft has put an end to point-to-point legacy integrations that hinder digital transformation. The Influence of MuleSoft and Salesforce Commerce Cloud: If the solutions that you are using currently do not allow you to tap into data from various sources, you're stuck inside one system. If you are willing to venture into new markets then it would consume more time and money. The right solution would be to combine your requirements in a single system, which is possible with Salesforce Commerce Cloud and Mule Soft which allows you to connect with other systems quickly. To Wrap-up: To succeed in today’s competitive era, businesses must create great customer experiences, which is possible by having ready access to customer data. However, brands are likely to stay behind if they fail to centralize their data. Salesforce and MuleSoft together are a perfect match as they both are dedicated to leveraging APIs and connectivity to help organizations succeed through digital transformation. To know more about the benefits of these platforms, it’s essential to get in touch with a certified Salesforce implementation partner.