Welcome To Our Blog

GenAI Workshop Success- How Gen AI Hands-On Learning Transformed Our Attendees

Anisha K Sreenivasan
June 12, 2024

The world is rapidly evolving, and with it, the sources of learning are expanding. We have access to a plethora of study resources on Generative AI (Gen AI), both online and offline. Online learning, in particular, has become the most sought-after method because it is time-efficient, accessible from anywhere, and can be customized to suit individual preferences. However, one must consider the stability and longevity of this learning. How proactive are you in your learning journey, and how long does the knowledge you gain last?

This is where the distinction between theoretical and practical learning becomes critical. While theoretical learning is essential for understanding foundational concepts, it is not sufficient on its own. True mastery is achieved when theoretical knowledge is coupled with hands-on learning, allowing you to gain practical experience and a deeper understanding of the subject, which is what Nuvepro is all about.

Are Hands-On Workshops the Key to Solving Real-World Problems?

When it comes to addressing real-world problems, hands-on workshops or hackathons prove to be highly effective. But why is this the case? How do these immersive experiences enhance project readiness, job readiness, and cloud readiness among learners?

Hands-on workshops and hackathons emphasize “learning by doing.” This approach immerses participants in the environment they will be working in, allowing them to build and practice new skills on the path to achieving competency. The Learning Pyramid shows that learners retain 30% more learning material and are more engaged when learning is hands-on. This highlights the critical importance of practical learning, which is often overlooked in corporate training programs, contributing to the job readiness gap.

How Is Generative AI meeting the Demand for Upskilling learners?

Generative AI (GenAI) is one of the most in-demand technical skills in today’s tech landscape. But how are we enabling learners to upskill effectively in this area? At Nuvepro, we have developed innovative solutions such as GenAI sandboxes and guided projects to bridge the gap between theoretical knowledge and practical application.

What Are Attendees Saying about Nuvepro’s GenAI Workshop?

To understand what drives successful upskilling, we conducted a poll among participants in our GenAI workshop. The results were telling: the majority of participants highlighted that hands-on learning through virtual labs or simulations is the most effective method for upskilling in GenAI, which is what Nuvepro had in mind when putting up the Gen AI workshop for upskilling/reskilling developers from various organizations. This approach increases skill retention rates and accelerates the adoption of new technologies.

Why Are Virtual Labs and Simulations So Effective?

The half-life of a skill is now shorter than ever, especially for technology-related skills. This rapid pace of change necessitates a new benchmark for what constitutes effective hands-on learning and virtual hands on training labs for skill validation.

Virtual labs are designed to build user competency by providing outcome-based scenarios based on real-world tasks. These hands on labs teach and validate technical skills simultaneously, ensuring that learners are not just passively absorbing information but actively applying it. Different types of virtual labs include sandbox and simulation environments. Virtual training labs mimic real-world software or platforms, allowing users to freely experiment and practice completing real-world scenarios. In contrast, simulated environments are more restrictive, allowing users to understand how software or tools behave under specific conditions.

How Do You Keep Your Skills Relevant and How Can You Benefit from the GenAI Workshop?

In an era where the shelf life of skills is decreasing, continuous learning and upskilling are crucial. The GenAI hands on workshop and hands-on labs are designed to keep learners ahead of the curve by providing the tools and environments needed to stay current with the latest technological advancements.

If you are looking to upskill in the field of generative AI, the GenAI workshops conducted by Nuvepro in partnership with AWS offers an unparalleled learning experience. By combining theoretical knowledge with practical application, you can develop a deeper, more comprehensive understanding of AI technologies. This hands-on learning approach not only enhances your technical skills but also prepares you for real-world challenges in your professional journey.

Innovations in Learning: Nuvepro’s GenAI Sandboxes and Guided Projects

Guided Projects: From Basics to Breakthroughs

Nuvepro has revolutionized the learning landscape with its Guided Projects, meticulously designed to bridge the gap between theoretical knowledge and practical application. These projects are tailored to help learners progress from mastering the basics to tackling more complex challenges. In a virtual environment that mirrors real-world scenarios, learners can run commands and perform tasks as they would in a live setting. This approach pushes learners to think creatively and develop innovative solutions to practical problems.

Guided Projects stand out from traditional self-paced courses and comprehensive learning paths by offering modular, bite-sized learning experiences. Each project is focused on specific use cases, allowing learners to test and apply their knowledge in meaningful ways. This method not only reinforces learning but also builds confidence as learners see the tangible results of their efforts. Nuvepro’s commitment to practical, project-based learning ensures that Guided Projects are a valuable resource for anyone looking to enhance their technical skills and knowledge.

GenAI Sandboxes: Secure and Controlled space for Experimentation

Gen AI sandboxes at Nuvepro are a game-changer for those looking to explore the capabilities of generative AI in a safe and controlled environment. These sandboxes for practice are integrated seamlessly into cloud infrastructure, providing pre-configured, hands-on labs that highlight various GenAI functionalities.

A notable feature of these Gen AI sandboxes is their design, which includes budget and service limits. This ensures that learners can experiment freely without the risk of incurring unexpected costs or damaging live systems. By providing a safe space to explore, test, and refine skills, GenAI sandboxes foster confidence and encourage innovation.

The GenAI Sandbox initiative was kick-started with a free two-hour Gen AI hands on workshop, where new learners had the chance to experiment with tools like Amazon Q without fear of making mistakes. These sandboxes for practice, powered by AWS Bedrock and Amazon Q, offer robust features and capabilities that are at the cutting edge of generative AI technology. Nuvepro’s solutions are adaptable and can be rapidly customized to integrate with existing information systems, enhancing data through the power of generative AI.

Maximizing Learning Outcomes with AWS Bedrock Sandboxes

AWS Bedrock: A Comprehensive GenAI Platform

As the demand for generative AI solutions grows, enterprises need robust, flexible, and secure tools to develop intelligent applications. Amazon Bedrock meets this need with a comprehensive suite of features that offer a user-friendly, scalable, and intuitive solution. AWS Bedrock provides all the foundational capabilities required to consume, fine-tune, deploy, and operationalize GenAI models.

AWS Bedrock is secure by design, ensuring that data stays within the customer’s environment and is not used for retraining. Its serverless architecture eliminates the need for provisioning infrastructure or scaling concerns, revolutionizing the handling of AI workloads. Amazon Bedrock’s customizable models and seamless integration with the AWS ecosystem make it an ideal platform for generative AI development.

How Gen AI Sandboxes for Amazon Bedrock Enhance Learning

Gen AI Sandboxes for Amazon Bedrock provide learners with a unique opportunity to delve deep into the world of generative AI. These Gen AI sandboxes offer a comprehensive suite of features that allow learners to consume, fine-tune, deploy, and operationalize AI models within a secure and scalable environment. The platform’s serverless architecture means learners don’t need to worry about infrastructure provisioning or scaling, allowing them to focus entirely on their learning objectives.

One of the standout benefits of Amazon Bedrock is its secure by design architecture. Data remains within the customer environment and is not used for retraining, ensuring privacy and security. This is particularly important for enterprises looking to develop intelligent applications without compromising sensitive information. With Bedrock, learners can choose from a variety of models to find the best fit for their use case. Our Gen AI hands-on workshop aimed to provide participants with practical experience leveraging foundation models through Amazon Bedrock.

Gen AI Sandboxes for Amazon bedrock also facilitate seamless integration with the AWS ecosystem, providing learners with a familiar and comprehensive environment to experiment in. The updated console makes it easy for developers to start building with Agents, streamlining the process and making it more intuitive for users at all levels.

Through these Gen AI sandboxes, learners can explore a variety of generative AI usage patterns. They can generate text and images, improve productivity by using foundational models for tasks such as composing emails, summarizing text, answering questions, building chatbots, creating images, and generating code. This hands-on learning experience is crucial for mastering new technologies and applying them effectively in real-world scenarios.

Redefining Software Development with Sandboxes for Amazon Q

Amazon Q is a game-changer for developers, generating code based on natural language prompts and simplifying the application development process. During our Gen AI hands on workshop, participants used CodeWhisperer to generate Python code for data cleansing and visualization, demonstrating how GenAI can streamline development processes and enhance productivity.

Learners now have an AI partner that assists with more than just code generation. CodeWhisperer helps with testing, debugging, and multi-step planning, making it an invaluable tool for software development and innovation.

How Gen AI Sandboxes for Amazon Q Transform Learning

Nuvepro’s sandboxes for Amazon Q , powered by CodeWhisperer, are redefining the way learners approach software development. These Gen AI sandboxes offer an innovative environment where learners can generate code based on natural language prompts, significantly simplifying the application development process.

During Nuvepro’s Gen AI hands on workshops, participants have used CodeWhisperer to generate Python code for tasks such as data cleansing and visualization, showcasing how GenAI can streamline development processes and enhance productivity. This practical experience helps learners understand how to leverage AI to automate repetitive tasks, allowing them to focus on more complex and creative aspects of development.

Gen AI Sandboxes for Amazon Q do more than just generate code; they assist with testing, debugging, and multi-step planning, making them invaluable tools for learners. By providing real-world inspired scenarios, these Gen AI sandboxes prepare learners for industry challenges, helping them develop innovative solutions and build applications more efficiently.

Hands-On Experience with AWS Bedrock and Amazon Q

Hands-on learning experience and experimentation are essential for mastering new technologies. GenAI Sandboxes offer a powerful way to facilitate this learning process, providing safe and isolated environments for users to test and refine their technical skills without affecting live systems.

At our recent GenAI workshop, we showcased the transformative potential of sandboxes for Amazon Bedrock and Sandboxes for Amazon Q (CodeWhisperer). These tools are at the forefront of GenAI technology, offering robust features and capabilities that drive innovation and productivity.

How Sandboxes for Amazon Bedrock Empower Learners

Sandboxes for Amazon Bedrock provide a secure, isolated environment where learners can explore generative AI without the risk of disrupting live systems. These environments allow for comprehensive hands-on learning experiences that are crucial for mastering new technologies.

Benefits for Learners:

Model Consumption and Fine-Tuning: Access a variety of pre-trained models and gain insights into customizing them to suit specific needs, deepening understanding of AI model intricacies.

Deploying and Operationalizing AI: Practice deploying AI models and learn how to operationalize them within a cloud infrastructure, gaining experience in the full AI application lifecycle.

Risk-Free Experimentation: Promote a “fail fast, learn faster” mentality, encouraging vigorous experimentation to discover the most impactful AI use cases without the fear of costly mistakes.

Understanding Foundational Capabilities: Explore core generative AI features, such as text and image generation, and advanced data processing and analytics.

Integration with AWS Ecosystem: Work seamlessly within the AWS ecosystem, utilizing Bedrock’s serverless architecture and secure design, ensuring data privacy and security.

In these hands on labs, learners engage with common generative AI patterns, boosting productivity with foundational models, and building practical applications like chatbots and automated email composers. The Gen AI sandbox environment fosters technical skill development and encourages creative thinking and innovative real world problem-solving.

Enhancing Development Skills with Amazon Q (CodeWhisperer) Sandboxes

Sandboxes for Amazon Q, powered by CodeWhisperer, are revolutionizing the approach to software development by providing an interactive platform for learners to develop, test, and refine their coding skills using generative AI technology.

Advantages for Learners:

Code Generation with Natural Language: Quickly generate code by describing what you need in natural language, accelerating the coding process and helping learners articulate their requirements clearly.

Hands-On Coding Practice: Engage in real-world inspired scenarios where learners can generate Python code for tasks like data cleansing and visualization, seeing the immediate impact of their commands.

Streamlined Development Processes: CodeWhisperer assists with various stages of development, including testing, debugging, and multi-step planning, making it an invaluable partner in the coding journey.

Enhanced Productivity: Automate repetitive coding tasks, allowing learners to focus on more complex and creative aspects of software development, thus boosting overall productivity.

Real-World Application: The sandboxes provide practical examples and projects that mimic real-world challenges, preparing learners for actual industry scenarios.

Safe Learning Environment: With pre-configured environments and budget limits, learners can explore without the fear of costly mistakes, encouraging bold experimentation.

Through these guided projects and sandboxes for practice, learners gain hands-on experience and develop a deeper understanding of how generative AI can be integrated into software development workflows. This practical approach ensures that learners are well-equipped with the cloud ready skills and knowledge needed to excel in the evolving tech landscape.

So, Are You Ready to Embrace Hands-On Learning?

Nuvepro’s GenAI hands on Workshop has proven to be a transformative experience for our attendees. More than 80% of attendees have successfully upskilled in Gen AI tools, becoming project-ready and job-ready for the rapidly evolving AI landscape. This hands-on approach has not only equipped them with practical knowledge but also empowered them to take control of their learning journey, fostering a sense of confidence and readiness to tackle real-world challenges.

As we move forward into an era where AI is becoming an integral part of every industry, the importance of experiential learning cannot be overstated. By stepping out of their comfort zones and embracing hands-on learning, our attendees have demonstrated the power of this educational paradigm. They are now better prepared to innovate, solve complex problems, and contribute meaningfully to the future of technology.

In conclusion, the success of the GenAI Workshop underscores a fundamental shift in how we approach education and skill development. The hands-on, experiential learning method has shown that it not only accelerates learning but also creates a more engaged and motivated cohort of learners. As we continue to embrace this future, we invite others to join us on this journey of discovery and transformation. The future of learning is here, and it is hands-on. Are you ready to embrace it?

Sign up for Newsletter

Our Latest Posts

Why Skill Validation Is the Missing Link in today’s Training programs

In 2025, We’re Still Asking: Why Isn’t Learning Driving Performance? Billions are being spent. Thousands of training programs are being launched every year. Yet here we are—facing a truth that’s too loud to ignore: learning isn’t translating into performance. Let’s pause and reflect. Have you ever completed a training, proudly received a certificate, and still felt unprepared for the real challenges at work? You’re not alone. Despite major investments in learning platforms and certification programs, enterprises continue to face a fundamental challenge: turning learning into measurable capability. It is no longer sufficient to rely on a model where employees complete courses and organizations hope those skills translate into performance. This “train and hope” approach has crumbled in the face of increasing business complexity, fast-changing technologies, and pressure for real-time results. Enterprises today are navigating a growing disconnect—the widening gap between upskilling and actual job readiness. While the number of training programs has increased, so has the frustration among team leads and hiring managers who realize, often too late, that employees are not ready to perform the tasks they were trained for. This gap is not just a training issue; it is a business risk. According to Lighthouse Research & Advisory, only 16% of employees believe their skills are being developed for future success. This alarming figure comes despite organizations pouring record-breaking budgets into Learning & Development (L&D). So where’s the disconnect? Why is the gap between learning and doing still so wide? The High Cost of Skills Gaps The urgency of solving this issue cannot be overstated. According to current projections, 85 million jobs may go unfilled in the next few years due to a lack of skilled talent. The estimated cost of this shortfall is a staggering $8.5 trillion in lost revenue globally. This is not a distant scenario but a rapidly approaching reality. Surveys reveal that while a majority of organizations—around 83 percent—acknowledge having skills gaps, only 28 percent are taking effective steps to address them. The reasons behind this gap are complex, but three consistent challenges emerge across industries: visibility into real-time skill levels, mechanisms to validate whether learning has truly occurred, and the ability to act quickly based on skill readiness. This lack of visibility, validation, and velocity is limiting the return on learning investments. More importantly, it’s hindering business agility in a world where time-to-skill is critical. What Exactly is Skill Validation? Let’s be clear—Skill Validation is not a buzzword anymore. It’s not just a new checkbox in the L&D strategy document. It’s a paradigm shift—a change in how we approach talent development, assess readiness, and ensure that learning has real-world impact. For far too long, training programs have been measured by inputs: But the truth is, none of these guarantees job readiness. You can complete ten courses on cloud computing and still struggle to set up a basic cloud environment. You can ace a leadership development program and still falter when managing your first real team crisis. Why? Because completing training doesn’t always equal competence. Skill validation flips the narrative. Instead of asking: “Did they finish the course?” We ask: Can they do the task in a real situation, or Can the person actually do the job when put in an actual project? Skill validation helps in true learning by doing There is a massive difference between knowledge acquisition and skill validation. It’s real practice that shows whether someone is truly ready. Skill validation is not about learning in isolation—it’s about learning in context. It’s about immersing learners in real-life scenarios, simulated environments, and hands-on tasks that mirror the challenges they will face on the job. What Does Skill Validation Actually Look Like? Skill validation can take many forms, depending on the role, industry, and level of expertise. Like, for example, In every case, the individual is not just recalling information—they’re applying it. They’re making decisions, solving problems, and adapting in real time. This is the kind of learning that sticks. This is the kind of learning that builds confidence. And most importantly, this is the kind of learning that prepares people for the unpredictable nature of work. Skill validation is: It ensures your employees aren’t just trained—they’re trusted.. Why Skill Validation Is a Priority Now The rapid advancement of technologies such as artificial intelligence, cloud computing, DevOps, and cybersecurity tools has shortened the shelf life of technical skills. Job roles are evolving so quickly that the lag between training and application can result in irrelevance. Moreover, threats such as security breaches or project failures demand instant readiness from employees, not a six-month wait to assess post-training performance. In this context, relying solely on traditional learning models is no longer viable. Businesses need to know—immediately—whether a new hire is ready to deliver or whether an internal employee is prepared for the next level of responsibility. Skill validation addresses this need by offering evidence-based assurance of workforce capability. Being “almost ready” isn’t enough in today’s fast-paced business landscape. Organizations need people who can deliver from day one. Project timelines are tight, customer expectations are high, and there’s little room for error. This is why skill validation isn’t optional anymore—it’s essential. It ensures your training efforts aren’t just about checking boxes. It ensures your workforce is not only engaged but equipped. It bridges the final and most important gap: from learning to performing. Integrating Skill Validation Into the Learning Ecosystem For organizations aiming to embed skill validation into their talent strategies, the approach involves three key steps: Establishing Visibility: The first step is to identify current skill levels across roles. This requires tools that go beyond static self-assessments and instead gather real-time performance data from immersive, task-based activities. Embedding Validation in the Learning Journey: Skill validation should not be a post-training activity. It should be integrated throughout the learning process—from initial assessments to final evaluations. This ensures that learning is anchored in outcomes, not just content completion. Enabling Agility Through Continuous Feedback: With validated data on individual and team capabilities, organizations can respond faster—by tailoring interventions, accelerating project readiness, or rerouting resources

Building a Skill Framework: Connecting the Dots Between Skills Taxonomy, Skills Ontology, Skill Families, and Skill Clusters

In today’s fast-evolving workforce, skills have overtaken degrees and titles as the true currency of value. With emerging technologies, shifting business models, and a growing gig economy, what a person can do has become more important than what they have done. Organizations now collect immense amounts of data on employee skills through assessments, performance reviews, learning platforms, and certifications. However, most of this data sits in silos—unstructured, underutilized, and often outdated. The challenge isn’t the lack of skills data; it’s the lack of a structured way to activate it. Without a clear strategy to interpret, map, and apply this information, organizations miss out on smarter talent decisions, agile workforce planning, and meaningful upskilling paths. To truly unlock the full potential of your workforce, you need more than just a list of skills—you need a well-structured skills framework. In this blog, we’ll walk you through how Skills Taxonomy, Skills Ontology, Skill Families, and Skill Clusters all fit together to build that structure. When used the right way, these tools can help you make sense of your skills data, close gaps, and prepare your teams for what’s next. What Is a Skill Framework? Imagine trying to build a house without a blueprint—or trying to manage your workforce without knowing what skills people actually have or need. That’s where a skill framework comes in. In simple terms, a skill framework is a structured system that helps organizations identify, organize, and manage the skills of their workforce. It works like a map—clearly showing what skills are important for each role, how different skills are connected, and where the gaps are. Instead of treating skills like a random list, a skill framework brings order, clarity, and purpose to your talent strategy. So, why does this matter? For HR professionals, Learning & Development (L&D) teams, and talent managers, a skill framework is incredibly valuable. Without a structured view of skills, it’s hard to answer basic but important questions: A skill framework helps answer all of these questions—and more. It becomes the foundation for smarter decisions across hiring, training, workforce planning, and career growth. Let’s look at some of the major benefits: First, it improves hiring. When you know exactly which skills are needed for each role, you can write better job descriptions, evaluate candidates more effectively, and reduce hiring mistakes. Second, it enables personalized learning paths. Instead of giving everyone the same training, you can tailor learning to each employee’s current skill level and career goals. This not only boosts engagement but also speeds up skill development. Third, it supports talent mobility. Employees often want to grow and move into new roles—but don’t always know what skills they need to get there. A skill framework shows them a clear path forward, helping them upskill and transition smoothly within the organization. And finally, it powers better workforce planning. With a clear view of current and future skill needs, organizations can prepare ahead of time—whether that means training, hiring, or shifting roles internally. In short, a skill framework turns scattered skills data into meaningful insights. It helps organizations not just understand their talent—but also shape it, grow it, and future-proof it. Understanding the Building Blocks Now that we know what a skill framework is and why it’s important, let’s break it down into its core building blocks. These are the key components that work together to give your framework structure, meaning, and power. Think of it like constructing a building—you need a strong foundation, a blueprint, organized rooms, and proper connections. Similarly, a solid skill framework is built on four essential elements: Skills Taxonomy, Skills Ontology, Skill Families, and Skill Clusters. Each one plays a unique role in organizing and making sense of your skills data. Let’s look at each one in simple terms: Skills Taxonomy: Bringing Order to the Skill Chaos One of the most important building blocks of any structured skill framework is the Skills Taxonomy. The term might sound a bit technical at first, but the idea behind it is actually quite simple—and incredibly useful. So, what exactly is a Skills Taxonomy? A Skills Taxonomy is a way to neatly organize all the skills in your organization into a structured hierarchy. Think of it like how you organize folders and files on your computer. You might have a main folder called “Projects,” with subfolders for each client or team, and then specific files within each one. A skills taxonomy works the same way—but instead of files, you’re organizing skills. Here’s how it typically looks: This kind of structure helps you create a clear, searchable, and organized list of skills across your entire workforce. It brings clarity to what skills exist, where they fit, and how they’re connected to job roles. Why Is a Skills Taxonomy So Important? At Nuvepro, we’ve worked with many organisations that already have skill data—but it’s often scattered, inconsistent, or duplicated. One team might call a skill “Project Management,” another calls it “Agile PM,” and a third lists “Scrum Master.” These are all connected, but without a structured system, it becomes hard to tell whether people are discussing the same thing. This is where a skills taxonomy makes a big difference. It gives everyone—whether it’s HR, L&D, or team leads—a common language to talk about skills. It removes guesswork and ensures everyone is aligned. When you say a role needs “Cloud Infrastructure,” it’s clear what specific skills that includes. No confusion. No miscommunication. Making Skill Inventories Work Suppose your organization wants to create a master inventory of employee skills. Without a taxonomy, you would likely end up with a long, unstructured list that varies from team to team. But with a skills taxonomy in place, you can organize that list in a way that’s logical and easy to manage. Here’s what a well-structured taxonomy allows you to do: This kind of structure makes it so much easier to: It’s not just about organizing skills—it’s about unlocking insights from them. Example: Building a Taxonomy for a Tech Team Let’s say you’re

The Storyteller’s 3-year Journey

Head of Marketing Shivpriya R. Sumbha, who recently completed 3 years at Nuvepro, looks back on her journey with grace, grit, and gratitude. Questions curated by Anisha Sreenivasan 1. How has your journey at Nuvepro been since April 2022? Any moments that stand out as turning points or proud achievements? Thanks, Anisha, for kickstarting the #PeopleAtNuvepro series—such a great way to reflect and share! Since joining in April 2022, the journey’s been full of learning, growth, and quite a few “wow, we’re really doing this” moments. We’ve evolved so much—not just in what we offer, but how we think about the value we bring to the table. There’ve been many initiatives that we’ve worked on, but for me, the proudest moments are when customers describe us not just for what we do, but for what we enable. When they see Nuvepro as a go-to for project readiness and skill validation—not just as a tool or a platform or divide our offerings and know us for 1 of it, but as a true enabler of Project Readiness – When they get that without us having to spell it out—it feels like we’re doing something truly right. That kind of recognition hits differently. 2. You’ve played a huge role beyond just Marketing Campaigns, workshops, hackathons, even sales outreach. How do you manage to juggle it all so well? Honestly, I don’t think it ever feels like we’ve “figured it all out”—and maybe that’s a good thing. There’s always more we can do, more ideas we haven’t explored yet, and that’s what keeps it exciting. We’ve done some great work as a team, no doubt, but I still feel like we’re only scratching the surface of what’s possible. Marketing, especially in a tech-driven company like ours, often plays the role of the silent enabler. Most of the spotlight naturally goes to the tech—and rightfully so—but behind the scenes, it’s been amazing to see how strategic marketing efforts have quietly shaped the brand, created visibility, and opened doors we didn’t even know existed. What I really hope to see in the coming days is Nuvepro being recognised not just for what we build, but how we’re building a brand that resonates—with customers, partners, and even within the team. We, are often attributed by the tech we create and not the way the brand has been overseen by the marketing efforts. Hopefully, we’ll see that day soon, too. 3. What was the most memorable event you worked on at Nuvepro-and what made it special? Of course, the first Nuvepro Project Readiness event was a huge success, and we all know it. That goes out to be my most memorable, and not because it was the first or because of the efforts put in. I was happy to know that the internal teams and management now know about the power of such event marketing strategies and how evidently they can bring us good connections. Striking that chord of confidence will always remain memorable. 4. As someone who built the marketing function from scratch here, what were your biggest challenges and learnings in the process? Initially the biggest hurdle was defining what marketing should look like in an enablement-driven, tech-first environment. There wasn’t a rulebook to follow—we had to experiment every few days on how we wish to be pursued. One of the key learnings was that marketing doesn’t have to be loud to be powerful all the time. Most of the brands and projects that I had worked for were on unmatchable performance marketing budgets but with Nuvepro I learnt that sometimes, the most impactful work happens in the background—crafting the right narrative, building relationships, or simply bringing organic consistency to how the brand shows up. It took time to shift perceptions—from seeing marketing as just promotion to recognizing it as a slow go-getter. It has made me learn about the organic growths too which are often overlooked in Marketing. 5. You have hosted several workshops, hackathons and roundtable conferences. What excites you most about these events? I guess connects and the post-event relationships that we build. We can simply set up a sales campaign or a PPC campaign and write sales ad copy, but we believe meeting someone and talking to someone establishes a much stronger relationship, and we aim to do just that. That excites me the most. The ability to network and build relationships through these events is truly good. 6. Beyond work, what are your go-to ways to unwind or recharge after a packed day of marketing magic? Now, since life has changed a bit, I like to read less, watch cricket a little less, stream less and indulge more in other things like #apartmenttherapy as you may call. I try out multiple recipes, I garden a lot more, I clean a lot more and learn many more things that I had never tried before. I always did all this before, too, now, with a unique zest. It is therapeutic for me to be a house runner; I love it, and I don’t wish it any other way. 7. Looking back at your journey from 2022 to now, what’s one piece of advice you’d give your past self? Haha just this one, “Your manager is a really good human first, and you will learn a lot, and you will have a great time in the coming few years, make the most of it, trust the process, don’t think you will not be able to survive 😊 ‘’ 8. You’re always full of energy as your colleague’s mention-how do you do that? At a very early point of time in life I have realized, our happiness and mood is our own responsibility, So I TRY to be not very much affected by the external factors, people, challenges and try to be in the best of moods always and the other thing is obviously, I love the idea of being approachable and friendly as a person. I obviously only try. 9. And

Welcome To Our Blog

GenAI Workshop Success- How Gen AI Hands-On Learning Transformed Our Attendees

Why Skill Validation Is the Missing Link in today’s Training programs

Building a Skill Framework: Connecting the Dots Between Skills Taxonomy, Skills Ontology, Skill Families, and Skill Clusters

The Storyteller’s 3-year Journey

Categories

Company

Partners

We Cater To

Legals

Resources