5 Top Managed Analytics Companies in eCommerce- Trusted by India’s eCommerce Businesses

The Indian and global retail / eCommerce industry is rapidly changing to adopt the latest technologies. Many sellers and store owners can benefit from eCommerce analytics. Here, we’ll discuss the top five managed analytics companies in eCommerce industry and their offerings. eCommerce is a fast-growing industry around the world. It is also rapidly evolving, with many business owners and sellers adopting the latest technology, be it artificial intelligence, PoS (Point of Sale) software, or data science models to streamline sales and make effective decisions in real-time. Statista reports that the global retail eCommerce sales worldwide account for more than $6 trillion and have over 20% of the market share.  Analytics are crucial for the eCommerce sector to overcome various challenges and reach out to the target audiences. From understanding demographics to analyzing customer feedback, data analytics supports e-commerce businesses to optimize their efforts and get improved results.  According to Business Research Insights, the global eCommerce analytics market is expected to touch $56.16 billion by 2031 at a CAGR (compound annual growth rate) of 14.51%. The same report states that the adoption of 4G and 5G technology will boost eCommerce analytics and encourage more business owners to invest in managed analytics.  In this blog, we’ll read more about e-commerce analytics solutions and the top companies to partner with for accessing managed analytics in real time.  What is E-commerce Analytics?  eCommerce analytics is the process of collecting, analyzing, and interpreting eCommerce data to derive actionable insights for decision-making. It helps in tracking and monitoring the KPIs (key performance indicators) to find effective ways to improve marketing, sales, and profits.  Managed analytics for eCommerce is a third-party service provided by data analytics companies to help businesses unlock the full potential of their data and derive useful insights. The business doesn’t have to build an analytical model from scratch or make huge investments. Instead, it can partner with a service provider to implement business intelligence tools like Power BI and access the reports through customized dashboards. This is a quick, easy, and effective option to gain a competitive advantage and make data-driven decisions to boost the business. 5 Top Managed Analytics Companies in eCommerce Here’s a list of the top companies offering online retail BI solutions to various establishments from the eCommerce industry. Some service providers offer additional solutions for comprehensive digital transformation.  DataToBiz DataToBiz is one of the top managed analytics companies in eCommerce, with years of experience working with businesses from the retail and eCommerce industry. The company has a presence in many industries with clients ranging from startups to large established brands. It uses advanced AI/ ML models, data science, and business intelligence tools like Power BI for eCommerce solutions.  DataToBiz is a certified partner of Microsoft, Google, and AWS and has an expert team to build, deploy, implement, integrate, and upgrade managed analytical applications for the client. The company also provides tailor-made data warehousing services along with cloud computing and migration solutions. Businesses can use customized sales, marketing, financial, and inventory dashboards to track KPIs and make proactive decisions to grab market opportunities.  The award-winning company is a one-stop solution for all eCommerce analytical needs. With transparent pricing, flexible partnerships, a diverse portfolio, and long-term support services, DataToBiz is a worthy choice for eCommerce businesses to adopt the data-driven model.  Saras Analytics Saras Analytics is a unified data and analytics company for the eCommerce, retail, and DTC sectors. It offers bespoke solutions and supports sellers in utilizing AI, ML, and advanced analytics to derive precise real-time insights. As an eCommerce solutions company, it helps businesses derive maximum benefits from data through business intelligence, data visualization, data engineering, etc.  Saras Analytics considers itself a team player and provides data team-as-a-service solutions for eCommerce and retail establishments to understand customer requirements, market trends, etc. The company increases customer experience and sales by streamlining business operations and optimizing resources. It aims to enable agile eCommerce decisions based on data-driven reports to minimize risk. Daton is a unified data platform developed by the company to facilitate the ease of global eCommerce services. The platform has over 200 connectors and provides eCommerce data insights to several clients.  RetailNext RetailNext was the first in-store analytics platform built to help businesses from the eCommerce industry. The company’s products are trusted by many leading brands from several countries. The solutions are scalable and can be customized to suit the requirements of any business, irrespective of the number of stores in the network. The company provides a range of services to handle different aspects of the eCommerce business, such as store design, operations, merchandising, marketing, and asset protection.  RetailNext has been developed by retailers to help modern sellers overcome different business challenges and achieve seamless results. Manage website traffic, online conversions, and more from an integrated platform. For example, Aurora is a next-gen sensor designed for physical location analytics. It uses a patented algorithm to combine deep learning and 3D imagery to deliver highly accurate results. The RetailNext solutions are pre-integrated to save time and can be easily implemented by all eCommerce businesses.  Palantir Technologies Palantir Technologies was founded to make AI products for human-driven analysis of real-world data. The company is focused on creating the best user experience for businesses to work with data and derive actionable insights. It is among the top managed analytics companies in eCommerce that offer AI-powered solutions to streamline operations and decision-making. The company has developed different solutions to help business owners effectively manage operations, sales, customer experience, etc.  For example, Gotham by Palantir Technologies is a commercial AI-ready operating system that accelerates decision-making for operators across different roles and departments. It is used by thousands of people and comes with multi-layered security. The built-in feedback loop trains and refines models used in the platform to ensure that it learns and trains itself to deliver better results over time. The company believes in offering full-stack interoperability through its platforms and gives the utmost importance to data security and data privacy. Streetbees Streetbees is an AI-based intelligence platform with top brands as clients. The eCommerce analytics company is disrupting market research by capturing real-life

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Decoding Q*: A Comprehensive Exploration for Both Technical and Non-Technical Minds

Today, we’re on our journey to decoding Q* (Q-Star), a name that sparks interest and curiosity. But what’s behind this mysterious combination of letters and symbols? What This Blog Will Cover Dive into the origin of Q* (Q-Star) and its intriguing blend of Q-Learning and A* algorithms. Ready for an adventure at the crossroads of machine learning and pathfinding? Let’s embark on this exciting exploration together! Let’s Go… Fusion of Q-Learning and A-Star The name Q* (Q-Star) likely draws inspiration from two powerful algorithms: Q-Learning and A* (A-Star). Let’s break it down for both tech enthusiasts and novices alike! Q-Learning Unveiled Q-Learning is like teaching a pet new tricks but for computers! Here’s the gist: Environment and Agent: Picture a video game or maze as the environment, and the AI as your in-game character. In simpler terms, it’s like mastering a video game by learning and adapting over time! A* (A-Star) Magic A* is a wizard in finding the shortest path between two points. Imagine it as your GPS for optimal routes: Now that we’ve got the basics, let’s get a bit speculative. How do these algorithms fit into the realm of large language models and AI? Decoding Q* (Q-Star) for Language Models Current large language models (LLMs) have limitations, especially in creative problem-solving and long-term strategy. Here’s where Q* comes into play: In the language model landscape, Q* could revolutionize how AI learns from interactions, improving responses, and adapting to new information and feedback over time. Excitingly, OpenAI’s breakthrough in Q-learning might just usher in a new era for large language models, overcoming current limitations and paving the way for unprecedented advancements in AI. It’s a thrilling journey where machine learning meets pathfinding, potentially transforming how AI systems tackle complex tasks. And who knows, Q* might be the missing piece for the next big leap in AI evolution! What are your thoughts on Q* and its potential?

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Top 10 AI Product Development Companies for Manufacturing

AI and ML solutions can revamp the manufacturing industry through real-time data analytics, IoT devices, digital twins, smart factories, and more. Here, we’ll discuss the role of AI and the top AI product development companies in manufacturing. Artificial intelligence is disrupting the manufacturing industry in many ways. Industry 4.0 promotes adopting technology in manufacturing to increase scalability, flexibility, agility, and production performance without spending more resources.  Many manufacturers from around the globe have been investing in AI in some form or another. Artificial intelligence has a versatile role in the industry, from automation to robotics to predictive maintenance. Statistics show that AI can increase productivity by over 40% by 2035. A report by Markets and Markets says the global AI in the manufacturing market will reach $16.3 billion by 2027 at a CAGR (compound annual growth rate) of 47.9%. The global AI market size was valued at $196.63 billion in 2023 and is expected to reach $1811.8 billion by 2030 at a CAGR (compound annual growth rate) of 36.8%.  Additionally, AI in manufacturing is useful in streamlining product development. Here, the term ‘product’ refers to the products manufactured by the enterprise as well as the software products companies develop to help manufacturers accelerate processes. Both can be achieved by partnering with AI product development companies and working together to gain a competitive edge in the market.  AI Product Development in the Manufacturing Industry Artificial intelligence has brought significant changes to product development in the manufacturing sector. Manufacturers can be more innovative, creative, and flexible in developing new products. AI also reduces time-to-market and allows enterprises to release products quickly. From ideation to production to warehousing, distribution, and marketing, every step is supported by artificial intelligence. For example, machine learning in manufacturing can analyze large datasets and provide detailed reports. Manufacturers use these reports and insights to understand customer requirements, market trends, etc., to make data-driven decisions about product development. Since it can be cost-intensive to build AI models from scratch, enterprises opt for third-party (offshore, onshore, and nearshore) AI product development services by reputed companies. This bridges the talent, skill, and technology gap in the enterprise while ensuring the budget and timeline constraints are followed.  Top AI Product Development Companies in Manufacturing DataToBiz DataToBiz is one of the leading AI/ML product development companies in manufacturing (and many other industries) with vast experience in building, developing, deploying, integrating, and customizing artificial intelligence models for enterprises. The AI product development company uses custom machine learning algorithms and advanced artificial intelligence tools to provide seamless product development and integration services. It helps manufacturers boost efficiency, use data analytical reports for insights, and personalize products based on customer feedback and market trends. The tried and tested six-step AI product development process ensures the promised results are delivered to the clients. Since the company provides long-term maintenance and upgrade services, manufacturers can become successful industry leaders.  DataToBiz offers comprehensive end-to-end solutions, starting from ideation to long-term maintenance. The award-winning company takes care of data engineering, AI/ ML development, cloud computing, big data analytics, generative AI, data visualization, data engineering, and many more. It focuses on helping clients align their business vision and goals with market requirements to gain a competitive edge and steadily increase revenue over the years. DataToBiz has flexible and transparent pricing suitable for MSMEs, emerging enterprises, and large-scale manufacturers from around the globe.  LeewayHertz LeewayHertz is a software development company offering tailor-made digital solutions to global clients. The company has a close-knit team of AI and Web3 developers who create next-gen apps to help businesses overcome challenges and achieve their goals. It works with startups, scale-ups, and enterprises to help them become tech-savvy and take advantage of AI products to boost their business.  LeewayHertz offers custom manufacturing software product development for automation, production planning, and remote asset management. The company combines technologies like blockchain and IoT (Internet of Things) for manufacturers to reduce operational costs without compromising quality. It provides an array of services as required by enterprises.  Cognizant Cognizant is a multinational company with over twenty-five years of experience. It is also among the top AI manufacturing development companies that help enterprises transform their business models and reimagine processes using the latest technology. From modernizing legacy systems to converting insights into action, the company provides a plethora of services to businesses.  Cognizant removes the barriers to innovation by integrating manufacturing processes with AI/ ML technologies. It promotes the use of IoT (Internet of Things) and generative AI for automation, real-time analytics, and more. The company helps enterprises build smart factories to transform traditional manufacturing into agile and future-ready methods. The company also helps clients improve sustainability and gives high importance to green practices.  Markovate Markovate specializes in offering AI and customized digital solutions to help businesses use technology to gain strategic advantage, enhance performance, promote change, and achieve goals. The company works with enterprises from the manufacturing industry to help them embrace world-class digital experiences through cutting-edge technologies. It designs ML frameworks and data analytical tools for scalability, reliability, and optimum performance.  Markovate uses generative AI, adaptive AI, and other advanced technologies to help manufacturers transform their vision into reality. From boosting productivity to increasing overall quality, the company supports enterprises at every stage and enables seamless adoption of AI and ML applications. It offers web and mobile application development, cloud computing services, and other solutions necessary for manufacturers to digitally transform their processes.  Builder AI Builder.AI is an AI product development agency that offers bespoke solutions to clients from different industries. It doesn’t believe in off-the-shelf apps, which may or may not be suitable for a business. The company has built award-winning apps and raised around $450 million to unlock the power of technology and enhance its role in boosting human potential. It has developed diagnostic software for the automotive industry to use real-time analytics for decision-making.  Builder.AI also builds CRM applications, OMS (order management systems), inventory management software, etc., to streamline various manufacturing processes. It helps automate recurring tasks to save time and resources and reduce operational costs. The company promotes AI product development

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Leveraging Large Language Models in Healthcare

Explore the potential of (LLMs) Large Language Models in healthcare, their applications, challenges, and prospects for improved diagnostics and patient care. Artificial Intelligence (AI) has ushered in a new era of possibilities, and at the forefront of this technological revolution are Language Models (LLMs). These powerful AI systems, equipped with deep learning algorithms, have the remarkable ability to understand, generate, and manipulate human language on an unprecedented scale. As we explore the potential of these models, we’ll delve into their applications, the challenges they bring, and the promising future they hold for advancing diagnostics and patient care. In recent years, the field of artificial intelligence (AI) has witnessed a revolutionary advancement with the emergence of Large Language Models (LLMs). With 30% of the world’s data volume being attributed to the healthcare industry, there exists a significant potential to harness the power of Large Language Models (LLMs) to operationalize and deliver new insights. These sophisticated AI systems, powered by deep learning algorithms, possess the ability to comprehend, generate, and manipulate human language at an astonishing scale. While their applications span various domains, the significance of Large Language Models in healthcare and clinical trials is particularly noteworthy. In this article, we delve into the realm of LLMs and their profound impact on the healthcare landscape, exploring their capabilities, applications, challenges, and future potential. Understanding Large Language Models At the core of this technological breakthrough lies the concept of Large Language Models. These models are intricate neural networks that have been meticulously trained on massive datasets containing diverse textual information. The training process equips them with the capacity to understand the nuances of language, context, and semantics, enabling them to perform tasks that were once considered exclusive to human intelligence. Unlike traditional language processing methods that rely heavily on rule-based approaches, LLMs rely on data-driven learning. This data-centric approach enables them to capture intricate language nuances and adapt to the ever-evolving dynamics of human communication. The shift from predefined rules to learned patterns marks a pivotal advancement in natural language understanding and generation. LLMs undergo training using vast datasets and employ self-supervised learning to predict the subsequent token in a sentence, taking into account the context around it. This iterative process continues until the model achieves a satisfactory level of accuracy. LLMs can be fine-tuned to perform a wide array of Natural Language Processing (NLP) tasks, such as: Creating interactive chatbots like ChatGPT. ● Generating text for purposes such as product descriptions, blog entries, and articles. ● Providing answers to frequently asked questions (FAQs) and directing customer queries to appropriate human support. ● Analyzing customer feedback found in emails, social media posts, and product reviews. ● Converting business content into various languages through translation. ● Sorting and categorizing extensive text data to streamline processing and analysis. Examples of Large Scale Language Models Three prominent examples of Large Scale Language Models are GPT-4, BERT, and T5. GPT-4, developed by OpenAI, is renowned for its creativity in generating coherent and contextually fitting responses. BARD on the other hand, developed by Google, revolutionized language understanding by considering the full context of a word within a sentence rather than processing words in isolation. T5, short for “Text-to-Text Transfer Transformer,” excels in various language tasks by treating them as text-to-text problems, demonstrating the adaptability of Large Scale Language Models. Applications of Large Language Models in Healthcare The integration of Large Language Models in healthcare has opened up a multitude of possibilities, transforming various aspects of healthcare delivery and research. Clinical Documentation and EHRs Efficient and accurate clinical documentation is essential for patient care. LLMs have the potential to automate the process of transcribing medical notes, generating detailed patient records directly from spoken or written input. This not only saves time for healthcare providers but also reduces the risk of errors in patient data. Literature Review and Knowledge Extraction The abundance of medical literature presents a challenge in staying updated with the latest advancements. Large Language Models can expedite literature reviews by summarizing articles, extracting key insights, and identifying relevant studies. This aids researchers, clinicians, and medical professionals in staying informed and making evidence-based decisions. Patient-Provider Communication Enhancement Effective communication between patients and healthcare providers is paramount. LLMs can assist in crafting personalized and comprehensible explanations of medical conditions, treatments, and procedures. This bridges the knowledge gap, enabling patients to make informed decisions about their health. Drug Discovery and Development The drug discovery process is complex and time-consuming. Large Language Models can aid in sifting through vast amounts of biomedical data to identify potential drug candidates, predict drug interactions, and assist in the design of clinical trials. This accelerates the drug development pipeline and enhances precision medicine. Clinical Trial Design and Execution Designing and conducting clinical trials requires meticulous planning. LLMs can contribute by analyzing existing trial data, predicting patient recruitment rates, and optimizing trial protocols. These insights facilitate efficient trial design and execution, leading to quicker and more cost-effective outcomes. Large Language Models Encoding Clinical Knowledge Enriching Large Language Models with domain-specific medical knowledge is a pivotal step in their healthcare applications. This involves training the models on vast medical datasets, encompassing information from electronic health records, medical textbooks, research articles, and clinical guidelines. The integration of medical expertise enhances the models’ ability to generate contextually relevant and accurate medical content. While imbuing LLMs with medical knowledge is promising, challenges arise due to the dynamic nature of medical science. Keeping the models updated with the latest research, ensuring accuracy, and addressing biases in medical data are ongoing concerns. Additionally, the inclusion of sensitive patient information demands robust data privacy measures. Pre-trained medical Large Language Models provide a foundation for various healthcare applications. These models arrive equipped with a baseline understanding of medical language, reducing the need for extensive training. By leveraging pre-existing medical knowledge, developers can focus on fine-tuning the models for specific tasks, streamlining the implementation process. Working Mechanism of Large Language Models Pre-training and Fine-tuning Process The proficiency of Large Language Models stems from a

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DataToBiz’s AI Solutions Pioneering the Path to Intelligent Automation

Emerging as a go-to partner for enterprises aiming to embrace the advancements in AI, DataToBiz acknowledges its immense potential and has curated a diverse range of AI solutions to cater to various business needs. “AI is the enigmatic force that blurs the line between imagination and possibilities, and we help businesses turn these exact possibilities into realities,” says Ankush Sharma, Co-Founder, and CEO, DataToBiz. DataToBiz, a leading AI solutions provider, empowers organizations with advanced artificial intelligence technologies, driving unparalleled efficiency, productivity, and innovation. Over time, the company is emerging as a critical driver for organizations looking to streamline processes, enhance decision-making, and unlock new business opportunities. DataToBiz recognizes the immense potential of AI in transforming industries and has developed a range of AI solutions that address diverse business needs. AI Solutions : Pioneering Intelligent Automation DataToBiz understands the competitive business landscape and acknowledges the significance of harnessing the power of AI into it. By providing customized AI solutions, DataToBiz empowers companies to unlock valuable insights from their data, enabling them to make informed decisions and drive innovation. Leveraging AI, their offerings encompass machine learning, in-app development, predictive modeling, and natural language processing, providing a path forward for their corporate partners. With a keen focus on SMBs, SMEs, and large enterprises across various industries, DataToBiz has established itself as a trusted AI consultant, or better- a partner. Renowned market players such as Dentsu, Tosoh Quartz, TransSIGHT, FTCSolar, Nordex, and more have implemented their advanced data solutions. At the core of their solutions lie advanced ML algorithms capable of analyzing vast volumes of data to unveil hidden patterns, trends, and insights. These algorithms continually and systematically learn from data, empowering businesses to make accurate and efficient data-driven decisions. Also, the expertise in natural language processing (NLP) is another critical aspect of their AI solutions. By leveraging cutting-edge NLP models, organizations can automate language-related tasks like sentiment analysis, content generation, and understanding. This automation saves time, and resources, and enhances customer experiences and engagement. DataToBiz- A Seal of Trust After recently completing a milestone of over 5 years in the industry, DataToBiz has found its place as one of the leading AI powerhouses sealed with the trust of various enterprises, in multiple industries around the world. With a proven track record of delivering innovative AI solutions, their expertise continues to drive technological advancements and reshape business landscapes. Along with featuring on Govt of India and MeitY, DataToBiz has proudly held the distinction of being Clutch’s top-ranked artificial intelligence company in India consistently for 3 years now. Not only this, with a user base of over 5.5k+ people around the world, DataToBiz has 2 in-house AI offerings: PrepAI, an advanced AI-based exam generator, and HirelakeAI, a turnkey AI-backed hiring solution. The Future Representation In a world where artificial intelligence is shaping the future of industries, DataToBiz stands as a trusted partner, delivering cutting-edge AI solutions that enable organizations to thrive in the era of intelligent automation. As the go-to choice for enterprises seeking AI advancements, DataToBiz is shaping the future representation of AI in business, creating a pathway toward transformative growth and success. Originally Published on Benzinga

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AI Product Development for Startup Apps- Top 5 Companies to Help You Get Started

AI empowers a business with the latest technology and uses analytical tools for decision-making. Startups need artificial intelligence to compete with brands that are already popular in the market. Here, we’ll discuss the top companies offering AI product development for startups. Artificial intelligence is disrupting every industry. Businesses around the globe are revamping their systems and processes to adopt the latest technologies to survive the ever-changing competitive markets. Organizations are either planning to digitally transform or have already invested in the process.  This makes it crucial for startups to include AI in their business model. Harnessing AI’s potential will be the deciding factor for many businesses. The global AI market is expected to grow by a CAGR (compound annual growth rate) of 36.38% from 2023 to 2030.  Another report says that the growth of AI startups has increased fourteen-fold since 2000. Moreover, 84% of senior leaders consider artificial intelligence essential to achieve business growth. The statistics reflect the increasing demand for AI in various industries. It’s not surprising that many startups ask questions like what is AI and how can it help their business.  In this blog, we’ll read how artificial intelligence can redefine your business and streamline internal processes, including product development.  Will AI Revolutionize the Product Development Process? Yes! Artificial intelligence is already revolutionizing product development by eliminating unwanted steps, optimizing resources, automating recurring tasks, and enhancing quality. In fact, AI product development tools can be used at every stage of product development, from ideation to delivery. Here’s how:  AI Product Development for Startups: Top 5 Companies DataToBiz DataToBiz is a leading artificial intelligence company with clients from several industries. The company offers AI product development for startups, SMBs, MSMEs, and large enterprises in different continents. It helps business organizations unlock the power of data and use its true potential to achieve long-term goals.  The award-winning company has many experienced AI engineers who worked on numerous projects of various complexities. With over 97% project satisfaction rate, the company provided a plethora of AI and ML solutions for 300+ firms. DataToBiz provides tailor-made AI services based on the client’s requirements. Be it platform and dashboard customization to pilot implementation, consulting, big data analytics, data engineering, digital transformation, and much more, the company knows how to handle the challenges faced by organizations.  It also provides affordable and cost-effective AI services for startups to help them gain a competitive edge among established businesses. The company is a certified partner of Microsoft, Google, and AWS and offers extensive cloud solutions. The team also builds and deploys large language models for generative AI-based applications.  Prosodica Prosodica is among the AI app development companies that focus on using the latest technologies in customer service. The company offers enterprise-grade analytics for speech and voice to help businesses understand conversions with customers and derive actionable insights. It has an AI platform to humanize the call center experience and help clients build a deeper and meaningful connection with customers. The platform works in four effective steps – collecting conversational data, running voice and speech analytics, deploying ML models to detect patterns, and providing solutions to reduce failures.  Prosodica’s conversation analytics platform can be integrated with various apps and software to streamline data flow. It offers future-proof technology and solutions to ensure organizations stay in tune with their customers’ needs. The company also helps boost ROI by enhancing customer experience. Simultaneously, it promotes employee performance by reducing stress caused by dealing with user complaints. The company’s solutions are used in various industries like healthcare, hospitality, financial services, etc.  DataVisor DataVisor is an AI product development team that provides real-time insights about fraudulent transactions. It uses applied machine learning capabilities to help businesses keep their clients safe from cyber abuse and fraud. The company has developed AI products to effectively handle modern-day fraudulent tactics and adopt preventive measures with agility. It has been recognized as the Best Transaction Fraud Monitoring and Decisioning Solution in the Datos Insights’ 2023 Fraud & AML Impact Awards.  DataVisor’s fraud and risk management platform helps mitigate cybersecurity concerns in real time. The company works with establishments from the finance, banking, FinTech, credit unions, and digital payment industries. From managing application fraud to preventing transactional fraud (wire transfer, check, etc.), the company offers consistent results and complete control to businesses. The AI platforms can be customized and aligned to suit the organization. The platform is hyper-scalable, easy to use, and has low latency.  Convr Convr is an AI-driven modern underwriting platform developed for insurance companies to enhance and automate their core internal processes. The company is founded by underwriters with vast industry experience to provide a reliable solution for insurance providers in the market. The company calls its platform a tool for underwriting on steroids. It is a modularized AI-powered underwriting and data analytics platform that also provides business classification and risk scores. Convr aims to solve the five biggest problems faced by commercial insurance companies- submission management, accurate business classification, gathering business information, identifying submission Q&A, and evaluating risk quality. It reduces 70% application questions and increases efficiency by 130%. The platform works by combining submissions with the best data sources to deliver reliable insights. The platform promotes transparency and speed. It can be used as an independent tool or integrated with third-party apps.  People.ai People.ai is an AI development company offering an enterprise revenue intelligence platform with its patented technology. It helps businesses speed up their sales cycles and to engage with the right audience at the right time. The company helps accelerate growth, innovation, and revenue. It provides the necessary insights for sales teams to focus on the right audience instead of chasing the wrong crowd. This reduces expenses while ensuring that interested leads are not ignored or forgotten. People.ai believes that the customer is everything for a business. It also feels that every problem has a solution. The company’s approach to this is quite simple. It follows a customer-centric approach to understand what the issue is and find a solution accordingly. The company has a presence in industries like IT, life

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A Comprehensive Guide to LLM-Based Model Development

AI products built with LLM-based model development have completely changed how computers understand and generate human-like text. These models, a type of deep learning tech, are super useful in things like chatbots and language translation. They’ve revolutionized how we talk to computers and extract relevant information. In this blog, we’ll explore what LLMs are, how to build one step by step and discuss some popular LLMs that have made significant impacts in the field. What is a Large Language Model (LLM)? A Large Language Model, often abbreviated as LLM, is a neural network-based model designed to process and generate human language. These models can understand, generate, and manipulate text with an astonishing level of fluency and coherence. They are considered “large” because they typically consist of tens to hundreds of millions, or even billions, of parameters, which are the elements that allow the model to learn patterns and associations in language. LLMs are trained on vast amounts of text data, often including books, articles, websites, and other textual sources. During training, the model learns to predict the next word in a sentence or to generate coherent text by understanding the statistical relationships and patterns in the data. This ability to generate human-like text makes LLMs a powerful tool for a wide range of applications. How Does a Large Language Model Work? Large Language Models, such as those based on transformer architecture, operate by a two-fold process: pre-training and fine-tuning. These phases are essential for enabling the model to carry out both general language understanding and specific tasks. Pre-training Large Language Models are initially pre-trained on vast text datasets containing a wealth of information from diverse sources like encyclopedias, books, and the internet. During this phase, the model undergoes unsupervised learning, where it absorbs linguistic patterns and contextual cues from the data without explicit guidance. It’s akin to the model immersing itself in the vast sea of language. For instance, it learns that “bat” can refer to a flying mammal or a piece of sports equipment based on the surrounding text. Fine-Tuning To make the model more task-specific, it goes through a fine-tuning process. This is akin to giving the model specialized training for particular tasks. It’s like preparing a chef with a general culinary skillset and then training them to excel in French cuisine or sushi preparation. Now, let’s explore “prompt-tuning,” which is similar to fine-tuning but with a twist. Prompt-Tuning Imagine the model as a versatile assistant that can perform a wide array of tasks. In prompt-tuning, we guide the assistant by providing specific prompts or instructions for different tasks. There are two key flavors to prompt-tuning:           Few-Shot Prompting: In this approach, the model learns how to respond to certain tasks by presenting it with a few examples. For instance, when training the model for sentiment analysis, you could show it pairs like: The model learns to grasp the nuances in language, connecting words like “incredibly thrilling” with positivity and “terribly boring” with negativity.  Zero-Shot Prompting This method tasks the model to perform a specific function without prior examples. It’s like handing a chef a new recipe they’ve never seen before and asking them to prepare it. For sentiment analysis, you might instruct the model with a prompt like, “Determine the sentiment of ‘The weather today is fantastic.’” The model, without any examples, deduces that “fantastic” conveys a positive sentiment. In both fine-tuning and prompt-tuning, the model becomes increasingly adept at performing tasks because it has refined its understanding of how to interpret and generate text based on the specialized training provided. Building Your Large Language Model To build an LLM model, you will need: ●      A massive dataset of text and code. ●      A powerful computer to train the model. ●      A deep learning framework, such as TensorFlow or PyTorch. Once you have these resources, you can follow these steps to train an LLM model: Collect and clean your data The first step is to collect a massive dataset of text and code. This dataset should be as diverse as possible, and it should contain examples of the types of tasks that you want your LLM model to be able to perform. Once you have collected your data, you will need to clean it and preprocess it. This may involve removing special characters, correcting spelling errors, and splitting the text into words or subwords. Choose a model architecture There are many different LLM architectures available. Some popular architectures include the Transformer and the GPT-3 family of models. The best model architecture for you will depend on the specific tasks that you want your LLM model to be able to perform. Train the model Once you have chosen a model architecture, you can start training the model. This process can take several days or even weeks, depending on the size of your dataset and the power of your computer. Evaluate the model Once the model is trained, you need to evaluate its performance on a held-out test dataset. This will help you to determine how well the model can generalize to new data. Deploy the model Once you are satisfied with the performance of the model, you can deploy it to production. This may involve making the model available as a web service or integrating it into an existing software application. However, building a Large Language Model from scratch is a resource-intensive endeavor, you can contact LLM consulting companies for guidance while developing the LLM Model. Using a Large Language Model Leveraging Large Language Models (LLMs) opens the door to a multitude of practical applications across various industries. Here, we will explore the diverse array of applications in which LLMs have found utility, along with real-world examples of their implementation. Information Retrieval Large language models play a pivotal role in information retrieval systems, akin to popular search engines like Google and Bing. These platforms utilize LLMs to fetch and synthesize information in response to user queries. By

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Behind the Scenes: A Look into Our AI Product Development Process

Discover how AI provides a seamless roadmap to overcomes the challenges in AI product development process, paving the way for innovation and exceptional possibilities? Well, let’s talk about that today!! The usage of AI in the product development process catalyzes innovation and enhances operational efficiency. It makes it easy to design, develop, and customize products to fulfill the customer’s needs. From transforming product development methodologies and outputs through personalized product recommendations and quality control, the scope of AI in product development is vast. In this blog, we’ll uncover the secrets behind our successful AI product development process. Let’s jump right in. Sneak-peak in the tools used for AI product development: AI Development: Tensor Flow, Amazon Sage Maker, Tableau, Keras, Microsoft Cognitive Toolkit, Cloud AutoML Vision, Apache MXNet Deployment: Azure, AWS, Google Cloud Platform, Nvidia Data Storage: Amazon Redshift, Amazon Athena, SAP, Azure Synapse Analytics, Azure Data Lake Storage, MongoDB, Microsoft SQL Server, Google Big Query, MySQL, Hadoop, Azure Integration Services Data Processing: Microsoft SQL Server, Talend, Azure Data Factory, Python, Spark, Kafka, Airflow, AWS Glue, and Databricks. Steps in AI Product Development Our AI product development team follows a six-step plan to create AI products. Our experts ensure a smooth transition as the output of one pipeline depends on the output of the other. Now, I’ll explain the steps in detail: Kicking-off the Project: The first phase of the AI product development roadmap involves defining the project scope and specifying what our experts will work on. Further, they choose the tools and technology stack and delve deep into user stories and use cases to find out how users will interact with the product. Next, the AI development experts create the system architecture blueprint along with the UI/UX design. Minimum Viable Product (MVP): Minimum viable product refers to a product version with sufficient features to attract early adopters and validate the product idea in the early stages. The AI developers prioritize the most important features and create a basic version of the product. Next, they build and improve the product in small steps known as iterative development, and ask the audience to use the product and share their feedback. Data Acquisition and Preparation: This third stage of the AI product development process includes gathering data and planning how this data will be used to build the product. The developers verify the accuracy and reliability of the data by cleaning and organizing it. Further, they add tags (if necessary) to categorize information and protect it from unauthorized access. Model Development and Training: Choose the right model and build the AI product that fulfills the specified needs. Transfer learning considerations, train the AI product, and test it to assess whether it meets the purpose it is designed for. Integration and Development: Integration and development is the fourth stage of AI product development. It includes API development, mobile apps, and web apps with a scalable architecture. It is important to ensure that the AI product works smoothly on various operating systems and devices. Quality assurance is also essential to verify that the product works efficiently and is free of bugs. Deployment and Maintenance: Deployment and maintenance are the best practices for AI product development to launch your AI product into the market. Once the product is launched, find out how the AI product is performing and take note of how you can make your product better. After you have made changes, provide the new versions of the AI product and maintain it to ensure smooth functioning. Benefits of AI Product Development Process Rich Computation AI enables the development teams to analyze huge amounts of data, detect patterns, and make predictions with machine learning techniques and algorithms. This makes it easy to build efficient AI products. Demographic Analysis With AI, developers can analyze data quickly and find new ideas. For example, you can use AI to understand customer preferences and recommend new features for a smartphone that people may like. Psychometric Analysis AI makes it easy to track and analyze data from different sources such as social media, search history, and physiological metrics. With a focus on ethical considerations, AI can be used to analyze data patterns and predict the probability of adoption of new products. Less Time AI significantly reduces the time needed for processes such as prototyping, testing, and development. The developers can create quick simulations, test them, and make improvements if needed. Informed Decision-making AI can quickly analyze large datasets and create multiple probabilities, thereby facilitating businesses to explore all aspects during decision-making. High Security: AI-powered authentication systems such as biometric scanning and CAPTCHA track security breaches, virus attacks, and malware. Also, the developers can use advanced tools to carry out investigative processes to trace vulnerabilities and prevent risks. Conclusion: Deploying AI into product development is the key strategy to optimize development processes and enhance productivity. AI product development companies help you to innovate, design, and launch AI products that remain competitive in the market. It is mandatory to abide by ethical considerations to ensure the sustainable growth of AI and implement effective strategies to redefine the future of AI product development. Originally Published on Medium

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How AI Can Help You Build Your Dream App!

We see artificial intelligence everywhere! It has a key role in app development too, be it speeding up the process or enhancing user experience. AI can indeed help you build your dream app or other AI/ML-based products and release it into the market. Let’s discuss how AI app development can be done. Artificial intelligence is no longer a distant technology. It is a part of our everyday lives and used by many people in one way or another. The advent of generative AI has led to major developments where almost anyone can create art, music, and apps without making heavy investments. While the results and ethical issues are debatable, we cannot ignore how AI has been integrated into our lives. According to Grand View Research, the global artificial intelligence market is estimated to grow by a CAGR (compound annual growth rate) of 37.3% and reach $1,811.8 billion by 2030. ChatGPT is the fastest-growing AI application, with over 180.5 million global users, of which 100 million are active weekly. This includes 92% of the Forbes 500 companies. Many individuals, entrepreneurs, and business organizations want to build an app with AI to suit their specific requirements. While this was expensive a few years ago, AI app development is now a cost-effective solution for building and deploying a customized application. In this blog, we’ll understand the role of artificial intelligence in building apps and see how you can build your dream app using AI. Role of AI in Apps Development AI app development is versatile and user-friendly. It helps streamline the ideation to implementation journey by reducing the risk of errors and speeding up lengthy processes. From automating code generation to debugging and reasoning, AI can be used in many ways. Machine learning and natural language processing are used to train the app to read and understand user input and provide relevant output. We live in a competitive world where businesses cannot afford to wait for months and years to release an application. AI for app development is a quick and effective solution to overcome this problem and take new apps to customers in less time. It’s no surprise that the demand for AI product/ app development companies have increased in recent times. Though you can use generative AI or ChatGPT to build your app, partnering with an experienced company will give long-lasting results. You can also hire their maintenance and support services to ensure customer satisfaction and higher ROI. Here are a few ways to use AI product development tools: How to Use AI for App Development Developing an application requires a lot of groundwork, coding, testing, documentation, etc., which leaves the teams on a tight schedule to get everything done in time. However, an AI product development team can streamline the tasks effectively and stay one step ahead. How, you ask? Find out below. Project Management Building and launching an app is a project, isn’t it? The success of your app also depends on how well you manage and organize the entire process. Artificial intelligence can help with planning, monitoring, measuring, collaborating, testing, and a lot more. It speeds up the process and increases the efficiency, accuracy, and performance of the app development team. For example, AI can automate recurring tasks, send alerts and reminders, schedule meetings, manage milestones, and support strategic data-based decision-making. AI also promotes innovation while reducing the risk of error. It makes collaboration easier, thus minimizing communication issues between team members. Automated Code Generation An app requires several pages of code. While all of it doesn’t have to be manually created from scratch (sections of code are taken from open-source and edited), the rest of the work falls on the programmer. With AI app development, most of the code can now be generated automatically. The programmer provides the input parameters for the system to write the code on its own. AI-generated code already adheres to the coding practices and brings more consistency to the project. While AI may not be 100% accurate with intricate logic commands, that part can be handled by the programmer. The boring sections can be auto-generated. It saves time and ensures human expertise. A win-win situation for everyone, isn’t it? Github Copilot is an example of an AI-powered code generation tool. Predictive Analytics Predictive analytics combines statistics and modeling techniques to predict an outcome for the given input. App developers run analytics on historical and present data to get insights about potential bugs and glitches that may arise during the project. From predicting how many lines of code will be written to calculating the resources required, predictive analytics helps app developers in many ways. Predictive analytics is a part of advanced analytics run using AI and ML models. Large datasets are analyzed to make data-driven decisions about project timelines, expenses, risks, opportunities, marketing, etc. Enhance User Experience How can you forget user experience? Keeping the end user happy with the app is the primary goal. While many want to create the best app for users, the execution is far from easy. AI can help here. AI product development companies use machine learning algorithms to analyze data and generate reports about user preferences. This gives them the metrics or indicators that help in adding features to the app. They are also useful for measuring the app’s performance during testing and debugging. Building apps backed by proper research will increase user experience, bring higher engagement, and establish the brand image. Natural Language Processing NLP is used to make a machine under human language in the proper context. It plays a vital role in bridging the gap between human and machine communication. NLP makes apps more intuitive and user-friendly. In fact, it is considered a reliable tool to create user-centric applications. Moreover, NLP-powered tools convert descriptions and feedback into actionable insights for the app-development team. It also automatically translates user requirements into an understandable format, making it easy for developers to customize the application for a wide range of audiences. An example of NLP in app development is a chatbot that responds

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5 Real-World Examples of AI Products in Action- From Start to Finish

AI products generate a lot of discussion. Despite the concerns, artificial intelligence is growing by leaps and bounds. Here, we’ll discuss some well-known real-world examples of AI products and their workings.  Artificial intelligence covers a variety of concepts like machine learning, deep learning, natural language processing, computer vision, etc. AI has become integral to our lives in recent times. AI is versatile and can be used in a plethora of activities, operations, and industries.  The global artificial intelligence market is over $136.6 billion, while the AI industry is expected to grow by 13x in the next seven years with a CAGR (compound annual growth rate) of 38.1%. Another report shows that 87% of global companies think AI gives them a competitive edge in the market. Be it in our personal or professional life, we cannot avoid using AI products in some form.  It’s no surprise that SMBs, MSMEs, emerging businesses, and large enterprises are partnering with AI product development companies to build and deploy custom applications powered by artificial intelligence.  This blog details real-world examples of AI products used by several people from around the globe.  What is an Example of Artificial Intelligence in Action? Almost half the businesses (48%) use some form of artificial intelligence, machine learning, and data analytics in their establishments. AI is meant for personal (individual), team, organizational, and global use. A few common examples of artificial intelligence in our day-to-day lives are listed below.  Text Editors Chatting, commenting, emailing, etc., are a part of our lives. The keypad apps in smartphones have built-in AI to auto-predict the sentences and emojis. Gmail also has an auto-predictor that suggests the next part of the sentence as you type.  Bloggers, content writers, and authors rely on AI text editors like Grammarly, ProWritingAid, etc., to streamline content. From identifying typos and punctuation mistakes to making suggestions about better sentence structures, the software improves the quality of writing. Artificial intelligence algorithms are trained on high-quality language data to identify errors in the input content and rectify them. The AI product development team also trains to understand the grammar rules and differences between different types of English.   Navigation and Maps Maps and GPS are some of the best real-world examples of AI products in action. Instead of using printed maps, we access the map and route through Google or Apple Maps. By connecting the phone to the car’s software, The GPS system will give directions in real-time. While GPS has been here for a while, the system has been enhanced by integrating it with artificial intelligence.  Machine learning algorithms are now taught to understand traffic flow, remember and recognize house numbers, and identify the best route to your destination. Taking things a step further, AI and ML are essential in self-driven cars, where two or more cars constantly communicate with each other.  Facial Recognition Facial recognition is another real-world AI product. The face lock security feature on smartphones is a perfect example of this. Many places, like airports, etc., use facial recognition to confirm your identity. It is a part of security and surveillance systems. The police use facial recognition software when checking leads and suspects to find the criminal.  Smart cameras come with this software to enhance the quality of selfies. Many apps like Snapchat, TikTok, etc., use it to add filters and customize images/ videos. Facial recognition is combined with AR technology for users to get a real-time experience in trying makeup products, eyewear, jewelry, etc.  Recommendation Algorithms Search algorithms are not limited to SEO. They are backed by artificial intelligence to provide personalized results based on your search history and preferences. For example, if you search for a handbag, you will notice relevant ads on your social media platforms and other websites.  Platforms like Facebook, Instagram, X, etc., train their algorithms to provide content based on the posts you like, comment, and share. Over time, the algorithms learn the behavior and tailor the feed accordingly. This is useful as you can avoid seeing content you don’t like. However, it can also feed you misinformation and half-truths.  Healthcare Real-world AI products are rampant in the healthcare industry, with many hospitals and pharma companies investing in advanced technology. Data shows that 38% of medical and healthcare providers use computer-aided diagnosis when treating patients.  Be it better diagnosis, personalized treatment plans, early detection of diseases, remote patient care, or faster drug discovery, artificial intelligence has a prominent role in the healthcare sector. Doctors are using robotic arms for complex surgeries. Mixed reality (MR) devices are also used to access patient information and get remote assistance during operations. Chatbots and virtual therapists are more examples of AI in healthcare.  Customer Service The customer service industry is quickly using chatbots to interact with customers. While the earlier chatbots had limited features, the ones powered by AI are more efficient in handling customer queries and complaints. Moreover, chatbots can interact with multiple users simultaneously and work 24*7 without tiring. This reduces the pressure on the representatives.  Businesses from every industry use chatbots in today’s world to enhance customer service. From tracking information, taking orders, answering queries, and generating tickets, the chatbots can handle several tasks with ease.  Electronic Payments Payment apps are well-known AI products in everyday life. Google Pay, PhonePe, Paytm, etc., are commonly used payment apps in India to make instant electronic payments by scanning a QR code. Many banks now use artificial intelligence algorithms for fraud detection to prevent scams and dubious transactions.  AI is also used for identity management, account management, and security management in banks, insurance companies, and financial institutions. The credit card spending patterns can be analyzed using artificial intelligence to understand the customer’s LTV and increase limits or offer loans.  What are the Real-World Examples of AI Products? Home Assistants Home assistants are the easiest example of AI products used in our day-to-day lives. Many of us have a home assistant device like Alexa, Siri, Cortana, or Google Assistant to play music, place an order, read out webpages, or provide answers to our questions by searching on the internet. They

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