Multimodal AI: The Convergence of Vision, Language, and Beyond

Multimodal AI is transforming artificial intelligence by integrating diverse data types—text, images, audio, video, and sensory inputs—into unified frameworks, enabling machines to process and generate richer, more contextual outputs. Unlike traditional unimodal models, Multimodal Large Language Models (MLLMs) combine multiple modalities, enhancing understanding, reasoning, and interaction capabilities. These models leverage advanced architectures, including multimodal input encoders, feature fusion mechanisms, and multimodal output decoders, to process and integrate data seamlessly.

Crypto Intelligence: How AI Agenting Powers the Blockchain

AI is now everywhere, transforming industries, by helping in automation at various levels, intelligence, and efficiency. From arts to healthcare to finance, AI has reshaped traditional methodologies with innovative and intelligent ones. In the world of blockchain and cryptocurrency it’s going to be game changer. With the transparency, security and decentralization in blockchain, AI is now helping in optimizing trading strategies, fraud detection, and automation of complex tasks. Let’s deep dive into the buzzing AI Agents, and how they advance the world of crypto intelligently!

The Need for Chain of Thought Prompting and Meta Reasoning Based LLM’s

In this ever evolving field of innovation and in the age of Artificial Intelligence , Generative AI has made significant leaps in terms of the impact it has had on the tech community. This sub field of AI , despite seeing exponential development and advancements is still in many ways nascent in it’s lifecycle , with many Large language models failing to perceive and answer logic and reasoning based , mathematics based , highly contextual or layered questions. There was a vacuum for chain of thought based LLM’s to come into the picture in order to systematically solve complex multi step problems, develop a train of thought and come up with more analytical and well thought out answers.

Breaking Down Data Silos: How AI-Augmented Data Engineering Powers a 360° Customer View in E-Commerce

As an e-commerce business, we struggled with fragmented data, leading to ineffective marketing and poor customer engagement. By adopting AI-augmented data engineering and a Data Lakehouse strategy, we unified out data, automated processes, and improved data quality. AI-powered cloud infrastructure optimized resource usage, predictive scaling, and error detection. This transformation enabled real-time insights, smarter customer segmentation, and data-driven decisions, boosting engagement and revenue. The next step is an AI-powered chatbot for instant, self-service analytics. The result: streamlined operations, reduced costs, and a 360° customer view, turning raw data into actionable insights and enhancing business performance.

Rethinking AI Hallucinations: Unlocking New Possibilities

Artificial intelligence (AI) is rapidly transforming industries, and Large Language Models (LLMs) are at the forefront of this revolution. While LLMs have demonstrated impressive capabilities in tasks like text generation and translation, they also exhibit a phenomenon known as "hallucination," where they generate factually incorrect or nonsensical outputs.

Facilitating Dark DevOps with ChatOps and Agentic AI

Advent of AI, IoT, Web 3.0, Quantum Computing, AR/VR has ushered in the era of 4th Industrial Revolution. As this 4IR world unveils itself, the boundary between the real and the digital world blurs. Humans prefer to interact with the real world using their virtual world and vice versa.

AI-Powered Platform Monitoring & Alerting: Evolution, Tools, and Business Impact

Platform monitoring and alerting have evolved from manual logs to real-time AI-driven analytics, ensuring businesses maintain uptime, security, and performance. AI has transformed traditional monitoring, allowing predictive analytics, anomaly detection, and automated incident responses. This blog explores the historical evolution of monitoring systems, the impact of AI, real-world applications, tools, and how organizations leverage AI for efficient platform management.

Transforming Product Management with AI: Insights for the Next Generation PM

Let's face it—product management is evolving faster than ever, and artificial intelligence (AI) is at the heart of this transformation. AI is empowering product managers (PMs) to make smarter, faster, and more data-driven decisions, fundamentally changing how products are built, launched, and scaled. From uncovering customer insights to automating repetitive tasks, AI is helping PMs focus on what truly matters: strategy, innovation, and delivering value to users. But with great power comes great responsibility—PMs must navigate challenges like data dependency, ethical concerns, and skill gaps to fully leverage AI's potential. In this blog, we'll explore how AI is reshaping product management, share real-world examples, and provide actionable insights to help the next generation of PMs thrive in an AI-driven world. The future of product management is here—are you ready to embrace it?

Rakuten's AI Revolution: Shaping the Future of Business, Technology and customer use cases

Rakuten solidified its position as a leader in AI innovation, driving transformative change across its global ecosystem. The company introduced several groundbreaking AI initiatives that are reshaping industries, empowering businesses, and enhancing customer experiences. These initiatives, including high-performance Japanese large language models (LLMs), Rakuten Link AI, and Rakuten Analytics, demonstrate Rakuten's unwavering commitment to pushing the boundaries of artificial intelligence.

Revolutionizing Retail: How LLMs Are Redefining Personalization

Retail has always been about understanding customers—whether it was the local shopkeeper who knew everyone's preferences or today's e-commerce platforms that cater to millions of users. Over the years, retail has transformed from a transactional experience to a relationship-driven journey, where customers expect businesses to anticipate their needs and deliver tailored experiences.

CAG vs RAG: Which AI Approach Reigns Supreme in 2025?

In the rapidly advancing field of artificial intelligence, Large Language Models (LLMs) are pivotal, especially for applications requiring document retrieval to enhance their capabilities. Retrieval-Augmented Generation (RAG) has emerged as a popular technique, combining the power of LLMs with the ability to access external data in real-time, addressing the limitation of classic LLMs that can only work with information available at the time of their training. RAG enables access to up-to-date and accurate information by retrieving external knowledge during inference, grounding outputs in real-world data.

AI in BI: Empowering Smarter Data Narratives through Data Intelligence

In today's fast-paced, information-centric world, businesses are constantly seeking ways to extract deeper insights and make smarter decisions. Traditional Business Intelligence (BI) tools, while foundational, often fall short in meeting the evolving demands of modern organizations. But what if AI could bridge this gap? By integrating AI into BI, businesses can move beyond static dashboards and manual processes to unlock dynamic, actionable insights. In this blog, we explore how AI and Data Intelligence platforms are reshaping the BI landscape, empowering organizations to harness the full potential of their data and drive innovation.

The Role of UX Psychology in Building Scalable Products

Scaling a product is often seen as a technical challenge—handling more users, data, and transactions. But what's often overlooked is the human side of scalability. No matter how robust the technology, a product can only scale successfully if it remains intuitive, engaging, and user-friendly. This is where UX psychology comes into play.

AI That Thinks Like You: How Cutting-Edge Technology is Reading Minds and Redefining Automation

Artificial Intelligence (AI) is advancing faster than ever, and the line between human thinking and machine intelligence is becoming thinner. What once seemed like science fiction machines that can think, predict, and even act like humans (Its nice if you remembered Jarvis or Friday) is now becoming a reality.

Transforming Marketing with Intelligent AI Agents

Generative AI is rapidly reshaping industries, and marketing is no exception. As businesses strive to connect with their audiences in more meaningful and personalized ways, the complexity of modern marketing has grown exponentially. From audience segmentation to crafting compelling ad content, managing budgets, and optimizing campaigns across multiple channels,

Customisation and Optimisation of Large Language Model

Large Language Models (LLMs) represent a transformative leap in artificial intelligence. Models like OpenAI's GPT, Meta's Llama, and Mistral are designed to generate and understand human-like text with remarkable versatility. Their popularity stems from their ability to learn and adapt across multiple tasks without requiring retraining for each new application. Traditional AI/ML models often need extensive domain-specific training and are restricted in scope, whereas LLMs, trained on vast datasets, are inherently more powerful and general-purpose.

How Gen AI is Disrupting Marketing Science & What You Need to Know

“Despite evidence that leadership in disruptive innovation pays such huge dividends, established firms, often fail to take the lead.” - Clayton Christensen, Innovator's Dilemma

Rakuten's AI Evolution from a Product Manager's Perspective

Rakuten's AI journey is reshaping technology, business operations, and customer experiences through innovative AI applications. With AI-driven business insights, customer engagement enhancements, and fraud detection, Rakuten is pioneering AI-powered solutions at scale. Collaborations like the "Rakuten AI for Business"

Beyond the Firewall- AI's Role in Shaping Next-Gen Cybersecurity for Enterprise SaaS

In an era where digital innovation unfolds at breakneck speed, the emergence of generative artificial intelligence (GenAI) stands as a testament to human ingenuity. With McKinsey Global Institute estimating that GenAI could contribute an astounding $2.6 to $4.4 trillion annually to the global economy, potentially increasing AI's economic impact by 15% to 40%. Yet, this bright horizon is not without its dark clouds, particularly in the realm of cybersecurity.

The Imperative Role of AI in Software Quality Assurance

Reflecting on the evolution of software development since the advent of agile methodologies reveals a significant transformation in software delivery practices.

Setting Boundaries: Where to Draw the Line When Companies Request Customization for a SaaS Product

In the ever-evolving landscape of Software as a Service (SaaS), one of the key selling points for businesses is the flexibility and adaptability of these cloud-based solutions. However, as companies seek to tailor SaaS products to their specific needs, a critical question arises: where do you draw the line when it comes to customization?

Enterprise SAAS - Are AI Agents disruptive?

Software as a Service (SaaS) business model represents how software is delivered, accessed, and monetized, and it has revolutionized the way software is delivered and consumed. The early 2000s marked the true birth of the SaaS model, catalyzed by the increasing availability of broadband internet and improvements in web technologies. It represents a shift from traditional software purchase and installation on individual computers or servers, to a subscription-based model where software is hosted in the cloud and accessed via the internet.

Riding the tech revolution wave: Strategies for staying competitive as an Enterprise SaaS Provider.

"Never before in history has innovation offered the promise of so much to so many in so short a time.” - Bill Gate

Unleashing the Power of Prompt Engineering with LLAMA 3

In the words of Jensen Huang, CEO of NVIDIA, 'Prompt engineering is transforming programming.' He believes that with the advent of generative AI, programming is no longer essential for success. Instead, he says, 'You just have to be a prompt engineer. And who can't be a prompt engineer? When my wife talks to me, she's prompt engineering me. … We all need to learn how to prompt AIs, but that's no different than learning how to prompt teammates.'

Navigating the Future: How AI Enhances Decision-Making in Enterprise SaaS

Imagine a world where every business decision could be backed by comprehensive insights and less guesswork. That world is already here. According to a recent report by Salesforce, 83% of IT leaders say AI is transforming customer engagement, and 69% say it is transforming their business operations. But how is AI specifically revolutionizing decision-making in Enterprise SaaS?

Unlocking New Frontiers: LLM's Transformative Potential Across Industries

The history of language models has seen rapid development marked by notable breakthroughs in recent years. Large Language Models (LLMs) represent a paradigm shift in Natural Language Processing (NLP) capabilities, and they are the result of this evolutionary journey within the field of language processing.

Revolutionizing Software Development: Harnessing AI in Software and Test Engineering

In today's rapidly evolving digital landscape, the quest for efficient and reliable software solutions is incessant. Traditional methods of software development and testing often struggle to keep pace, resulting in delays and increased costs. However, amidst these challenges, artificial intelligence (AI) emerges as a transformative catalyst, reshaping how software is conceived and scrutinized. AI goes beyond mere buzzwords, heralding a paradigm shift with profound implications for the software engineering landscape.

LLMs in Action: Real-World Applications Revolutionizing Industries

"With LLMs, we are witnessing the dawn of a new era, where machines become our linguistic partners, shaping thoughts into eloquent expressions." The advent of Large Language Models (LLMs) has revolutionized the way we interact with and generate language in the digital realm. Over the past few years, LLMs have gained significant traction, thanks to ground-breaking advancements made by companies like OpenAI. These state-of-the-art models have been trained on massive datasets, enabling them to comprehend and interpret human language with remarkable accuracy and fluency.

Age of Multimodal Models

The field of artificial intelligence has seen remarkable advances in recent years, with language models achieving human-parity performance on many natural language processing tasks. However, a new frontier is emerging with the development of multimodal large language models (LLMs) that can process and generate not just text, but also images, audio, and other modalities. These AI systems have the potential to revolutionize human-computer interaction, enabling seamless multimodal communication and unlocking new applications across industries. From automating multimedia content creation to enhancing accessibility through audio- visual AI assistants, multimodal LLMs represent a paradigm shift in how we conceive of and interact with intelligent systems. As this technology continues to evolve, it promises to reshape our digital experiences and push the boundaries of what is possible with AI.

AI-nization in Observability

In today's ever-changing technology landscape, it is imperative that systems remain reliable and perform optimally. To achieve this, it has become vital to monitor and understand the internal state of systems, a practice known as observability. Artificial intelligence (AI) has revolutionized observability by enabling proactive issue identification and resolution, as well as predicting future infrastructure usage for effective resource scheduling, thereby avoiding overprovisioning.

Advertising with Artificial Intelligence (AI)

Having spent nearly seven years in the ad tech industry, I've witnessed first-hand the remarkable transformation of digital advertising. What started as static displays has evolved into a dynamic landscape, now heavily influenced by AI. Over the years, I've seen how advertising has shifted from merely eye-catching visuals to a nuanced understanding of consumer behaviour and personalized targeting.

Deciding between RAG and Fine-Tuning: finding the right approach for modal Adaptation.

In the world of language models, making the right choice between retrieval-augmented generation (RAG) and fine-tuning is crucial for achieving optimal performance...

Design Thinking for Enterprise SaaS

In the age of speed, instant gratification, automation, productivity, and delightful experiences, you've likely encountered a term that is being aired more frequently than ever before: Design Thinking...

The AI Revolution: What's New and What's Next for Large Language Models

As the world becomes increasingly data-driven and digitized, the ability to communicate effectively with machines has become a critical necessity. At the forefront of this endeavour are large language models, innovative AI systems that are redefining the way we interact with language itself...

Autonomous Operations – IT operations on Autopilot

Imagine a bank outage that lasts for days, leaving almost a million deposit accounts inaccessible and causing significant financial hardship for customers. Such incidents are not uncommon in the ever-evolving world of information technology. Despite efforts to address IT complexity, outages and performance issues are still prevalent. However, a technological solution to a social problem is reshaping the approach to IT complexity with a different perspective.

Can AI Help Businesses Retain Customers in Fiercely Competitive Markets?

Researchers have found that the costs associated with acquiring new customers can be five times (or more) higher than those associated with maintaining relationships with existing customers. Thus, once acquired, businesses should use customer relationship management (CRM) to nurture customer relationship to prevent them from churning. In this blog, I have explored the user aspects of customer interaction with an ecommerce business and how we could use it for Churn prediction and eventually prevention so that we are able to improve the customer experience and create a win-win situation for both marketer and the customer.

Transforming Data Management: Leveraging Large Language Models and Data Mesh for Enhanced Analytics

In the digital age, data has emerged as a cornerstone of organizational success, driving decision-making, innovation, and competitive advantage across industries. From e-commerce giants analyzing consumer behaviour to healthcare providers optimizing patient care, data-driven insights have become indispensable for achieving strategic objectives and staying ahead of the curve in today's hypercompetitive market landscape...

Data Mesh Principles

In today's Data world, the current landscape can be divided into two parts : Raw Data and Analytical Data. Raw Data is stored in storage clusters, databases, disk units behind business capabilities...

AI: How it is Classified and where it is used in Today's Tech World

In today's tech industry we see and hear a lot about these “buzz” words like AI, ML, GenAI and LLM .So I would like to give a distinguishing difference about what each of these technologies mean and how do they differ from each other...

The future of CRM with the power of AI: Salesforce CRM

Salesforce, the #1 AI CRM, enables companies to connect with customers through a unified Einstein 1 platform that combines CRM, AI, Data, and Trust. This combination of CRM, AI, and data is what creates the Salesforce magic. From the start, Salesforce has been on a mission to help businesses succeed, and to use business success to drive positive change for the people.

How AI can help solve problems of IT infrastructure professionals

AI seems to be taking over the world right now. The pace of innovation with AI is surpassing any other innovation in the history of mankind. It is almost like a genie out of the bottle and is fulfilling the wishes of many. So if you are an IT infrastructure person, I am sure your boss would have called you up by now and asked one of these questions: “What are we doing about AI?”, “ How can we use AI to solve our problems?”, “How can AI be used in infra?” ,“ Everyone is talking about GenAI, why are we not doing anything about it? “ When can we roll out Ainization programme in our department? So if you are currently doing tonnes of Google searches or asking GenAI to give you ideas , here's a blog that can help you save some time. While the field itself is evolving at a rapid pace, this article will give you some clues that you can use to investigate further.

Bulk Data Transfer Using Open-Source Solutions

One of the interesting problems in Data Platform space is how to bulk transfer Petabytes of data correctly from On-prem systems to Cloud at a very low cost. Different organizations go for different solutions ranging from open source tools to enterprise grade tools . In this article, we would be majorly focusing on open source solution and how we can leverage these solutions to build an in-house product.

Building Large Scale AI/ML Platform

2024 is an year of GEN AI and LLM. Today, everyone is talking about building their own GEN AI solutions and using it for their business needs. What about the Platform or Infra ? Nobody wants to manually deploy 10's / 100's of models by manually logging into the machines. How can we automate this process and make it self – serve ? This following article discusses on the same topic.

AI Revolution: Friend, Not Foe! How AI is Empowering Humanity!

It was one fine winter day on November 30, 2022, that OpenAI unleashed ChatGPT to the public. The “AI” buzzword, hitherto relegated to being jargon used by nerdy PhD labs and the tech elites or a marketing catchphrase used by neo-techno startups to woo VCs, had transcended its niche origins to become a household name. It captured the public imagination like no other, going on to become the fastest-growing consumer software application in history by January 2023. It reached about 100 million active users in just 2 months after launch! In comparison, Netflix took 20 years to reach the 100 million-mark. People were mind-blown by what they saw; they could ask any question and get the most relevant answer instantly, solve coding questions, get help with homework, find food recipes, and even plan their travel. People were discovering new use cases with ChatGPT which the original creators never thought possible. AI had truly arrived!

Software testing completeness with AI

Software testing is challenging when it comes to validate a software with traditional software testing concepts. The software testing is more dependent on the experience and knowledge in the test area as required for complete coverage...

AI Is Not Just Data Sciences

AI is no stranger to workplaces today. Fifty-three percent of global leaders have integrated or are integrating AI into their workforce to enhance their business insights. While IDC has predicted that by 2022, 75% of enterprises will have embedded intelligent automation into technology and process development, the key aspect to consider is how and through which job roles organizations are going about incorporating AI in their workforce.

Ensuring Successful Scaling-up Strategy for your Analytics Product

As per the International Data Corporation's estimates, the global data volume would reach an astounding 175 zettabytes by 2025. To help you understand the magnitude a little better, it is 175, followed by 21 zeros! Not all of this data is useful and extracting business insights from huge swathes of data is akin to looking for a needle in a haystack.

The Power of AI in Imaginary Worlds

It is frequently said that AI is unable to express creativity or emotions, and that these limitations prevent it from accurately replicating a human. However, has expanding technology allowed us to overcome yet another obstacle? Is it accurate to say that AI is creative?

Unleashing Creativity – AI and The Future Of Fashion

The fashion industry is on the brink of a revolution as generative AI emerges as a game-changer in design, production, and marketing. This year New York City organised its first ever AI fashion show and it couldn't have been more revolutionary. AI has officially sweeped into every industry there could be and it's coming to reality way too soon because of the coming of Generative AI. Technology and innovation have always been at the forefront of the fashion business, whether it's via the adoption of new materials to support sustainability or the development of trend- forward trends and predictions. It comes as no surprise that AI has begun to enter the fashion sector.

Intelligent Engagement: Revolutionizing Lead Nurturing with Machine Learning

Every month ~0.8 million leads visit group of Rakuten Insurance websites. The marketing team wishes to nurture high potential leads to convert them into a customer...