1. What motivated your organization to participate in this year’s Global AI Awards?

We participated in this year’s Global AI Awards because we believe generative AI is entering a critical stage of real-world deployment, and high-quality data infrastructure has become one of the key factors determining whether AI innovation can scale responsibly and globally. As a company focused on multilingual and multimodal AI data services, we saw this as an opportunity to share how data-centric innovation can accelerate the development of large models while also supporting safety, inclusiveness, and practical adoption. The awards also provided a valuable platform to showcase the role that foundational data plays in the broader AI ecosystem. For Nexdata, participating was not only about recognition, but also about contributing to a global conversation on how AI can be developed more effectively, responsibly, and with broader societal value.

2. Could you give us an overview of the AI solution or breakthrough you submitted for consideration?

Our submission focused on Nexdata’s generative AI data infrastructure and service capabilities designed to support the training, alignment, and deployment of large language models, multilingual models, and multimodal generative AI systems. At the core of this solution is our ability to provide scalable, high-quality data resources and delivery systems for advanced AI development. We have supported more than 1,000 global enterprise clients and built ready-to-use resources including petabyte-scale LLM datasets, tens of millions of hours of speech data, and hundreds of terabytes of visual data. In addition, our human-in-the-loop annotation platform and pre-annotation engine have been applied across 5,000+ projects, helping improve efficiency in generative AI data workflows by more than 30%. Our work also extends to emerging model alignment needs, including RLHF structured data delivery at scale, as well as multilingual and cross-cultural data support across 200+ languages and 80+ countries.

3. How did your team collaborate to develop and refine this AI innovation?

This innovation was built through close collaboration across data engineering, annotation operations, quality management, product, and AI domain teams. Developing scalable generative AI data solutions requires far more than raw data collection—it depends on aligning technical standards, workflow design, linguistic expertise, and quality assurance into one integrated system. Our teams worked together to refine annotation methodologies, optimize pre-annotation workflows, improve delivery efficiency, and strengthen quality-control mechanisms for complex generative AI use cases such as multilingual model training, multimodal learning, and RLHF-based alignment. We also collaborated closely with clients and research-facing initiatives, including our MLC-SLM challenge and workshop, which helped us better understand evolving industry needs in multilingual conversational AI and Speech LLM development. This cross-functional and ecosystem-oriented approach enabled us to continuously improve both the technical robustness and practical applicability of our AI data solutions.

4. What impact do you expect your AI work to have on the broader AI community or society as a whole?

We believe our work can have impact at both the industry and societal level. For the AI community, our contribution is to strengthen the underlying data foundation that enables generative AI to move from experimentation into reliable, scalable, and globally deployable applications. By reducing barriers to access high-quality multilingual and multimodal data, we help more organizations and research teams build and improve advanced AI systems efficiently. For society more broadly, we see long-term value in making AI more inclusive and representative. Our support across 200+ languages and 80+ countries contributes to the development of systems that are less concentrated around a small number of dominant languages and regions. We also believe responsible data practices are essential to ensuring AI benefits can be extended safely and sustainably. Ultimately, our goal is to help make generative AI more useful, accessible, and globally relevant.

5. Were there any notable challenges during the development of this AI solution, and how did you overcome them?

One of the biggest challenges in generative AI data development is balancing scale, quality, and diversity at the same time. Large models require enormous volumes of data, but quantity alone is not enough—data must also be accurate, representative, well-structured, and aligned with increasingly complex downstream tasks. Another challenge is that generative AI use cases evolve very quickly. Requirements for multilingual understanding, multimodal reasoning, conversational speech, and human preference alignment continue to grow, which means data pipelines must adapt just as fast.

We addressed these challenges by investing in scalable human-in-the-loop workflows, pre-annotation technologies, standardized quality-control systems, and global data supply capabilities. We also built mechanisms for continuous refinement through project feedback, domain expertise, and research engagement. This allowed us to improve operational efficiency while maintaining quality and supporting new AI development needs as they emerged.

6. How does your organization nurture a culture that drives continuous AI innovation?

At Nexdata, we see innovation as something that comes from combining technical rigor with openness to new real-world challenges. We encourage teams to stay close to both industry demand and research developments, so innovation is driven not only by internal ideas, but also by practical needs emerging across the AI ecosystem. Our culture emphasizes cross-functional collaboration, experimentation, and continuous improvement. Teams are encouraged to refine workflows, test new methods, and translate project insights into scalable capabilities. We also support knowledge exchange through external engagement, including workshops, challenges, and research-oriented collaboration, which helps us stay connected to the broader frontier of AI development. Most importantly, we treat data not as a commodity, but as a strategic enabler of AI progress. That mindset drives us to keep improving quality, efficiency, and global applicability in everything we build.

7. What advice would you offer to teams or companies aiming to make meaningful contributions in the AI space?

Our advice would be to focus on real problems, not just trends. The AI field moves quickly, but meaningful contributions usually come from solving foundational challenges that affect long-term performance, reliability, and adoption. It is also important to recognize that strong AI systems are built on strong infrastructure. Data quality, workflow design, evaluation, and responsible governance are just as important as model architecture. Teams that invest in these foundations are often the ones that create lasting value. Finally, think globally and responsibly from the beginning. AI products and models increasingly serve diverse users, languages, and contexts. Building with inclusiveness, transparency, and scalability in mind will not only strengthen technical outcomes, but also improve the real-world relevance and impact of the work.

8. What are your organization’s long-term goals in AI, and how do you plan to advance the field moving forward?

Our long-term goal is to become a foundational enabler of global AI development by continuously strengthening the data infrastructure behind generative AI, multimodal AI, and multilingual intelligence. We want to help make advanced AI systems more scalable, more inclusive, and more responsibly deployable across industries and regions. Moving forward, we will continue expanding our capabilities in large-scale multilingual and multimodal data, model alignment support, conversational AI data, and higher-efficiency data production workflows. We also plan to deepen our contribution to the research and developer community through initiatives such as benchmarks, challenges, and cross-sector collaboration. As AI adoption accelerates worldwide, we hope to play a long-term role in advancing the field not only through commercial delivery, but also by helping shape better standards, broader access, and more sustainable innovation.

9. Are there any emerging AI technologies or trends your team is particularly excited about right now?

We are particularly excited about the next phase of multimodal and multilingual generative AI, especially the convergence of text, speech, image, and real-world interaction in more unified model architectures. We believe this will open up major opportunities for more natural, accessible, and context-aware AI systems. We are also closely watching the rapid development of Speech LLMs and smaller, more efficient language models that can deliver strong performance in specialized or resource-constrained environments. These trends are especially important for global deployment, where language diversity, latency, cost, and device constraints all matter. At the same time, we are encouraged by the growing focus on model alignment, evaluation, and responsible AI. As the field matures, we believe the most important breakthroughs will come not only from making models larger, but from making them more reliable, inclusive, and useful in real-world settings.

To dive deeper into Nexdata’s award-winning work, visit their website at https://www.nexdata.ai