Welcome to HackerNoon’s Meet the Writer Interview series, where we learn a bit more about the contributors that have written some of our favorite stories.So let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests.My name is Abhishek Kumar. I work in business intelligence and data science within the logistics industry, where much of my work involves data engineering, analytics, dashboards, and building applications around operational data. I am also pursuing a PhD in Information Technology with a specialization in artificial intelligence. A lot of my interests naturally sit at the intersection of AI, data, and real business problems. Outside of work and research, I enjoy traveling, following current affairs, exploring new technologies, and spending time with family and friends.Interesting! What was your latest HackerNoon Top story about?My latest story, “How I Would Build an AI Assistant for Real-Time Shipment Visibility,” came from a problem I have seen firsthand while working with logistics data.Shipment information can be spread across status events, locations, depots, partners, and other operational datasets. My article explores how I would put an AI assistant on top of that information without allowing the AI to become the source of truth.The central idea is fairly simple: let the database provide the facts and let AI make those facts easier to understand. I also discussed some of the less glamorous but important parts—cleaning shipment timelines, handling duplicate events, controlling what the model can query, preventing hallucinations, and keeping humans involved when a decision has real operational consequences.Do you usually write on similar topics? If not, what do you usually write about?This is actually the beginning of my HackerNoon writing journey, so I would not say that I have a usual topic here yet.That said, the article represents the kind of subjects I want to write about: practical AI, logistics technology, data engineering, analytics, enterprise AI, and the problems that appear when we try to take an AI idea from a demo into a real business environment.I am especially interested in the gap between “this works in a prototype” and “this can actually be trusted in production.” There is a lot to write about in that gap.Great! What is your usual writing routine like (if you have one?)I normally start with a problem rather than a title.Most of my ideas come from something I encounter at work, during research, or while experimenting with a technology. I usually write down the problem first, then think through how I would explain it to someone who understands technology but may not know the specific business context.My first draft is usually messy. I would rather get the idea on the page first and then clean it up. After that, I go back through it, remove unnecessary technical language, verify the details, and ask myself whether each section actually adds something useful.I also try to include the things that did not work or could go wrong. In technology writing, I think those details are often more useful than presenting everything as a perfect solution.Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing?For me, the biggest challenge is finding the right level of technical depth.If I go too deep, the article starts reading like documentation or an academic paper. If I simplify too much, it becomes another high-level technology article that tells people AI is “transforming everything” without explaining how anything actually works.I experienced that even while writing my shipment-visibility article. There were many technical details I could have included about data models, queries, event processing, security, and model orchestration, but the real challenge was deciding which details actually helped tell the story.Time is the other challenge. Writing has to fit around professional work, doctoral research, and everything else happening outside both.What is the next thing you hope to achieve in your career?I want to keep moving closer to work where AI, data engineering, and real operational problems come together.I am particularly interested in building AI systems that go beyond a chatbot interface—systems that can work with enterprise data, use tools, understand business context, and still remain governed and explainable.Academically, I also want my PhD research to complement what I am learning in industry rather than exist separately from it. Long term, I would like to contribute to technology initiatives where research ideas can actually be tested against real-world problems and eventually turned into useful systems.Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice?Probably falling into a YouTube rabbit hole when I originally opened it to watch one video.I can start with something related to technology or current events and somehow end up an hour later watching travel videos, documentaries, interviews, or something completely unrelated to what I was supposed to be doing.Do you have a non-tech-related hobby? If yes, what is it?Traveling is probably the biggest one. I enjoy visiting new places, trying different food, and just getting away from the normal work-and-study routine.I also follow current affairs quite closely. It may not technically qualify as a relaxing hobby all the time, but I enjoy understanding what is happening beyond the technology world and hearing different perspectives on it.What can the Hacker Noon community expect to read from you next?I want to continue exploring practical AI from the perspective of someone actually working with enterprise data.There are several directions I would like to take next: AI agents in logistics, how to evaluate AI models for enterprise use cases, hallucination and safety monitoring, open-source model adoption, AI governance, and what happens when agentic systems are allowed to interact with real business tools.I also want to write more about the data side of AI. The model gets most of the attention, but in many real projects, getting the data, business logic, permissions, and architecture right is the harder part.What’s your opinion on HackerNoon as a platform for writers?What attracted me to HackerNoon is that it sits somewhere between highly formal technical publishing and short-form social media.You can go deep enough to explain how something works, but the article can still be conversational and based on personal experience. That format works well for people like me whose primary job is not writing but who have technical experiences and lessons worth sharing.I also like that there is room for practitioners to write about what they are actually building, testing, and learning rather than only covering technology after it has already become established.Thanks for taking time to join our “Meet the Writer” series. It was a pleasure. Do you have any closing words?Thank you for having me.I am still at the beginning of my HackerNoon writing journey, so having my first work reach readers and lead to this interview is encouraging.One thing I hope to carry into future articles is the same principle I try to follow in my technical work: start with the real problem. Technology changes quickly, and there will always be a new model, framework, or AI trend to discuss. What makes those technologies interesting to me is figuring out where they are genuinely useful, where they fall short, and what it actually takes to make them work outside of a demo.I look forward to sharing more of those experiences with the HackerNoon community.Check out Abhishek Kumar’s HackerNoon profile here, and read more of his amazing stories!https://hackernoon.com/u/abhi12295