Dan Ciorăscu (Clara AI): From transportation to AI with real impact

Dan Ciorăscu, co-founder and COO of Clara AI, discusses his career journey, the challenges of building scalable AI products, and how Clara AI creates real value in the transportation and logistics industry.

Dan Ciorăscu is the co-founder of Clara AI and serves as Chief Operating Officer (COO), playing a central role in defining and executing the company’s operational strategy. With a career built entirely within the technology startup ecosystem and now in his second entrepreneurial venture, he has held roles ranging from product manager and product architect to executive leadership positions. Currently, he oversees the commercial operations of Clara AI, contributing directly to the scaling of AI-based automation solutions for the transportation and logistics industry.

In the following, Dan Ciorăscu will share, among other things, insights into the challenges of developing Clara AI, how Clara AI differentiates itself from the rest of the market, and how it came into being.

C&B: If we were to look at the narrative thread of your career, which were the key moments that defined you?

Dan Ciorăscu: Looking back, the narrative thread of my career can be distilled into one defining moment: the decision to transition from a family business in road transportation to the technology startup ecosystem. My professional formation began early in this sector, where, starting at the age of 14, I was directly involved in operational and technical activities—from hands-on work in automotive mechanics, a constant passion of mine, to coordination and management roles in transportation. This early exposure gave me a deep understanding of on-the-ground realities, operational discipline, and the responsibilities that define a functional business.

The turning point came with my entry into the tech startup environment, facilitated by Cristian Pîrvan, a colleague and partner who became a close friend. It was a profoundly different professional context, with a much faster pace and considerably more complex challenges. The transition was, first and foremost, a shift in perspective: from a traditional business built within a local logic to the ambition of creating a technology product with global relevance, moving from local competition to direct confrontation with international—especially American—startups that were far better funded.

C&B: What are the biggest challenges in developing and scaling an AI-based product?

Dan Ciorăscu: The biggest challenge in developing and scaling an AI-based product is, in reality, creating added value, not simply using the technology. Today, many solutions stop at the level of an “LLM wrapper,” meaning an application that places a language model behind it but does not truly build a product, a business logic, or a clear advantage for the user.

A simple analogy is to think of an LLM as a shovel. It is a very powerful tool, but on its own, it produces nothing. Value only appears when there is a “mining company” behind it—meaning a well-built application layer that knows where to dig, what to look for, and what to do next with what it extracts. The added value is not in the tool itself, but in how it is used.

A solid AI product almost always starts with clear specialization: a well-defined niche, a precise use case, and a real, concrete problem. This value is not built quickly. Even with significant capital available, it takes time to understand the domain, the underlying processes, and the real needs of users. The maturity of such a product comes from years of iteration, testing, and continuous adjustment in direct contact with reality.

From this perspective, AI is not the product itself, but a function of the product, integrated into a broader system. The major challenge is building that application layer: the architecture, rules, and business logic that make the model useful, predictable, and relevant in users’ day-to-day lives. Without this layer, AI remains generic, easy to copy, and difficult to turn into a real advantage.

Ultimately, building and scaling an AI product is not about having the best “shovels,” but about knowing exactly which problem you want to solve and how to use technology to generate real value for people and for businesses.

C&B: Is there a dream or ambition that has always guided you, regardless of obstacles?

Dan Ciorăscu: If I were to identify a constant that has guided me regardless of context or obstacles, it would be the desire to continuously learn. More than a clearly formulated objective or a “dream” in the classic sense, it has always been the need to expose myself to new things, to situations that take me out of my comfort zone and force me to understand, adapt, and grow. I have consistently sought contexts in which I do not have all the answers from the start, because that is precisely where real progress happens.

This approach pushed me to change fields, accept difficult transitions, and enter professional environments with a high level of uncertainty, where learning is not optional but a condition for survival. Each stage brought with it a new set of problems, different people, and perspectives that expanded the way I think. Over time, I came to see obstacles not as something to avoid, but as a necessary mechanism for development.

In essence, the ambition that guided me was less about a final destination and more about the process: to keep learning, remain curious, and constantly place myself in situations that force me to evolve.

C&B: What was the most difficult decision you made in the early stages of Clara AI?

Dan Ciorăscu: The most difficult decision in the early stages of Clara AI was related to the direction of development: whether to scale horizontally or to build depth vertically. At the beginning, the company started with a single product—automating the recruitment process for professional drivers using AI Voice Agents. Essentially, instead of a human answering calls generated by recruitment campaigns, there was a voice agent that speaks multiple languages fluently, is available at all times, understands role requirements at the level of a human recruiter, and can conduct pre-screening interviews, filtering candidates and directing them toward hiring when appropriate.

From this point, we had two clear options. The first was horizontal development: to take the same recruitment product into as many blue-collar domains as possible, from warehouse handling to equipment operation or similar roles. The second option was vertical development: to use the technology already built to mature the product and extend automation to other critical processes within transportation companies, such as training and dispatch.

We chose the second option, guided by the belief that deep specialization creates real value and long-term differentiation. At the time, the decision was difficult because it meant giving up an apparently faster and broader expansion. Looking back, however, this choice proved to be the right one: it allowed us to build a much more mature product, anchored in the realities of the transportation industry, and to develop a coherent platform rather than just a point solution.

C&B: If we were to meet your team or collaborators, what do you think they would say about you?

Dan Ciorăscu: I think that if you spoke with my team or collaborators, they would tell you that, at first, I can be hard to read. It is not always clear whether I am speaking seriously or using my rather dry sense of humor. I ask a lot of questions, I am fairly reserved, and at times I can seem tougher than I actually am.

As we work together, things settle. The sarcasm becomes easier to understand, intentions clearer, and my focus on solutions and results starts to stand out. I do not try to impress or be the “character” in the room; I prefer to be useful and coherent. They would probably say, with a smile, that I am a bit atypical and sometimes uncomfortable, but that beyond the style, they can rely on me when things get complicated and direction and decision-making are needed.

C&B: What is the most important decision you have made that changed your trajectory?

Dan Ciorăscu: The most important decision that changed my trajectory was choosing long-term construction over short-term comfort. More specifically, I decided to remain in a space of continuous uncertainty, where almost nothing is guaranteed, instead of optimizing an already functional and predictable setup. This choice meant taking on constant risks, giving up quick solutions, and accepting that real progress comes from slow, sometimes frustrating, but cumulative iterations. The decision was not spectacular at the time, but it had a profound effect: it completely recalibrated the way I think, make decisions, and relate to failure, transforming uncertainty from an obstacle into a permanent working environment.

C&B: Which mistake offered you the most valuable lesson so far?

Dan Ciorăscu: The most valuable lesson came from a mistake that Cristian and I made before Clara AI, within a previous project called Clevgo Driver. It was another company, another product, but the same founding team. Although at that time we had early signs of traction, we later realized that much of it was artificially built, because it was based on a flawed premise.

The major mistake was distancing ourselves from customers. Instead of staying close to them, understanding their real problems, and seeing what their workday actually looks like, we chose to remain “closed” in the office and imagine the market’s needs. Both Cristi and I have vivid imaginations, but experience showed us very clearly that imagination cannot replace on-the-ground reality.

The lesson was simple but fundamental and profoundly influenced our subsequent way of working: stay close to customers. Assume nothing. Let them guide your product, because only that way can you build something that solves a real problem and deserves to exist.

C&B: What differentiates Clara AI from other existing AI solutions, and what is its main value for users?

Dan Ciorăscu: Clara AI’s differentiation does not start from a classic comparison with other existing solutions, because the market we operate in is still very young, both in terms of use cases and competition. Rather than entering a “better than” logic, we prefer to talk about being “different in approach.” What we are building is not a point AI functionality, but an operational layer that embeds deeply into the infrastructure of transportation companies and automates processes that, until now, have depended almost exclusively on people.

The main value for users is extremely pragmatic and easy to understand: being able to operate more trucks with the same number of people. Clara AI takes over repetitive and time-consuming interactions with drivers—whether in recruitment, training, or daily operations—and turns them into scalable, always-available, and fully traceable processes. In this way, existing teams are not replaced, but relieved of operational pressure and can focus on decisions and situations that truly require human intervention.

In essence, Clara AI does not promise abstract efficiency or “AI for the sake of AI,” but a very concrete outcome: operational growth without proportional growth in headcount, in an industry where the shortage of people is already a structural problem.

C&B: What does a typical day look like for you now, and which moments bring you the greatest satisfaction?

Dan Ciorăscu: Honestly, I could not say that there is a “typical day” in the true sense of the word. Since I started working in technology startups, every day looks different, and the agenda is constantly influenced by rapidly changing priorities, new contexts, and problems that cannot be anticipated. If a clear routine ever appears, I promise to come back with a more structured answer to this question.

What is constant, however, are the moments that bring me the greatest satisfaction. They are almost exclusively related to growth: when we add a new client, when we launch a feature that moves the product forward, or when we manage to visibly improve the quality of the services we offer. These are concrete signs that the work put in translates into real progress, and they are the moments that give meaning to the intense rhythm of each day.

C&B: What values or principles guide you in what you do, and how do you apply them day to day?

Dan Ciorăscu: The principle that guides me the most is continuous learning. I constantly try to expose myself to new things, to contexts I do not yet master, and to people I can learn from, because that is where real progress appears. In practice, this means not starting from assumptions, accepting feedback, treating mistakes as sources of information, and being willing to adjust my direction whenever reality demands it.

C&B: How did the idea for Clara AI emerge, and what real market problem pushed you to build this product?

Dan Ciorăscu: The idea for Clara AI emerged naturally from the experience accumulated during Clevgo Driver, a recruitment platform for professional drivers. Working closely with clients’ recruitment departments, we began to observe the real operational difficulties they were facing. One concrete example was a client who, after publishing a job ad, received around 80 calls in a single day. While, in theory, this should have been a success, in practice it revealed the real problem: teams’ inability to efficiently handle, process, and filter such a large volume of calls.

From this operational bottleneck, the idea for Clara AI’s recruitment product was born—as a solution that could take over repetitive and time-consuming interactions and turn them into a scalable process. Subsequently, the rest of the products emerged organically, following the same logic: starting from real problems signaled by customers and building solutions directly based on their needs, not on assumptions.

C&B: How is the market reacting to Clara AI, and what feedback confirmed that you are on the right track?

Dan Ciorăscu: The market reaction has been very positive, and the clearest signal in this regard is a simple but extremely relevant indicator: zero churn. It may sound hard to believe, but so far we have not had a single client give up on Clara AI. For us, this is the strongest possible feedback, because it does not come from statements or initial enthusiasm, but from consistent usage and from clients’ decision to continue.

Dan Ciorăscu’s story is one of long-term building, discipline, and continuous learning. From hands-on experience in transportation to creating an AI product deeply rooted in industry realities, his journey reflects a pragmatic, value-driven approach focused on real impact.