AI that ‘loves’ Humans: How Omdena’s Human-Centered AI Platform is Defining the New Era of AI Development

When Omdena launched in May 2019, the goal was ambitious but simple: create a global platform where people could collaborate, learn, and apply AI to solve real-world problems. At the time, AI had not yet reached today’s level of mainstream adoption, but the passion for its potential was evident.

Over the next four years, Omdena grew into a thriving, diverse community of 30,000 AI enthusiasts spanning over 120 countries, executing 600+ AI projects. Many of these projects made a real-world impact, but at this stage, Omdena was primarily known as an educational and prototyping platform—a space where AI engineers could gain experience while building early-stage AI solutions.

This model worked well for learning and experimentation. However, as companies sought to transition from prototypes to scalable AI products, a new challenge emerged: how could Omdena evolve from a learning-focused platform into a world-class AI development ecosystem capable of delivering production-ready solutions?

Through deep reflection and analysis, we identified four key challenges that needed to be solved to transform Omdena from an educational initiative into a professional AI execution platform.

Challenges in Developing AI Solutions from Prototype to Production

1. Imbalanced Skills in the Project Teams

Omdena’s collaborative model brought together passionate AI engineers from around the world. While this created a dynamic and collaborative environment that is essential for ethical AI development, it also introduced challenges in structured, production-level AI development. Many contributors excelled technically but lacked experience in professional workflows, team building, communication, and other soft skills.

Given our focus on productizing AI solutions, where adaptability and teamwork are crucial, communication gaps and varying soft skills sometimes affect project outcomes. As a fully remote platform, ensuring clear collaboration became essential. To improve project success, we needed a better way to vet and structure teams, selecting contributors not just for technical expertise but also for their ability to communicate, collaborate, and adapt effectively.

2. Lack of Real-Time Visibility and Quick Identification of Bottlenecks

Unlike traditional software development, where milestones and deliverables are often well-defined, AI projects—especially innovative ones—can progress non-linearly, where experimentation is key, and results don’t always appear immediately.

While companies working with Omdena understood the risks of innovation in principle, the lack of visible, measurable progress in practice created uncertainty. Without clear ways to track advancements, some clients became concerned when weeks passed without tangible outputs.

In some cases, their concerns were justified—teams hit bottlenecks, but due to the decentralized nature of collaboration, these roadblocks did not always surface quickly. We needed a transparent system that could track project evolution in real-time, allowing both teams and clients to identify obstacles early and adapt efficiently.

3. No Clear Link Between AI Tasks and Code Contributions

One of the biggest gaps in AI project execution was the disconnect between task management and actual code development.

Like in software engineering, Omdena teams used task boards such as Kanban to track progress. However, clients—or even project managers—could not directly see how specific pieces of code aligned with assigned tasks. This lack of visibility made it difficult to assess the project's status beyond subjective reports from developers.

For AI development to scale, we needed a system where every line of code was traceable to a specific task and deliverable, providing full transparency to both engineers and clients.

4. No Continuous Feedback and Lack of Growth for the Project Team

Omdena’s mission was always about empowering talent, but as projects grew in complexity, it became clear that educational opportunities needed to evolve alongside execution.

Most developers entering Omdena were incredibly talented but lacked experience in production-level AI development. There’s a significant difference between writing code in a research setting and building AI models that integrate into real-world systems. Without structured, continuous feedback, many developers weren’t able to refine their work to match enterprise standards.

To bridge this gap, we needed a systematic approach to providing mentorship and code reviews, ensuring that developers weren’t just gaining experience but also leveling up their skills in a structured way.

The Solution: Building a Human-Centered AI Platform: The Future of AI Development

At Omdena, we believe AI should empower people, not replace them. Therefore, we refer to AI as ‘Augmented Intelligence’ rather than ‘Artificial Intelligence’. Our focus was never on replacing developers but on enhancing their work output and solving our own internal challenges.

When we wrote our internal AI Governance framework, we mentioned that the human element would be key to tackling ‘bad’ AI. Our framework was based on the three Cs: Collaboration, Compassion, and Consciousness.

Collaboration among varied talents enables us to bridge gaps in understanding between different mindsets, share knowledge, and unite people and values. It, therefore, helps to create compassion and harnesses crowd wisdom, diversity, and inclusion to serve the long-term interests of those communities. The other key element is consciousness. As so much division exists in this world, we need to understand that deep down, we all are one. Thus, our consciousness is collective. Through forming a sense of community, we collaborate together with compassion and consciousness. Thus, AI built by the three C’s (Collaboration, Compassion, and Consciousness) will help us to remove endemic sociological and historical bias and other inequalities that exist in society.

This philosophy led us to develop a Human-Centered AI Platform designed to enhance human intelligence, promote ethical AI development, and ensure transparency in AI solutions.         

So, in a nutshell, we needed the platform to:

1. Help us identify the right ‘collaborative’ people for a given project: Given in Omdena, we do not just build AI models but also start projects at the grassroots level and upskill people; we have a ton of data to understand the growth and skills of a person. We have seen that in today’s AI world, where things move so fast, knowledge is not the most important, as one can easily gain the necessary knowledge. However, the most important is motivation to grow and the ability to learn fast. A person with good knowledge but extremely high motivation and ability to learn will outperform a person with better knowledge but medium motivation.
2. Transparency at code level that connects to task: The question that we asked ourselves is, can we build tools to have full transparency where any code written can be automatically connected back to a given task? It also tracks how much of the code is able to achieve the goals of the task, thus giving us (and the client) a way to know the progress of any task. We wanted to build a more objective tool that not only the project manager but also the client can use and rely on to understand the progress of the tasks. While building the tool, our goal was not to have something that is 100% accurate but something that will flag, say, 90% of the cases correctly and then let a human expert go into those cases to check. Our methodology is to let the machine do what it does best, crunching massive data and analyzing the codebase while the project manager (or the client) can review the flags and take action. This tool will also help us (and the client) quickly identify possible bottlenecks ASAP and fix them before it's too late (before spending too much time).
3. Automatic feedback: Code refactoring has its place and will continue to have a place, but it is done once in a while. What about the time between two code refactorings? Given the kind of engineers we had in Omdena, we needed a more thorough feedback process for the engineers so that they could learn fast.

Why Existing AI Development Tools or Platforms Weren’t Enough

As we scaled our AI projects, we assumed that existing project management and collaboration tools would address the challenges we faced. Other AI teams must have encountered similar issues, so surely there were platforms designed to streamline collaboration, track progress, and improve AI development workflows.

To our surprise, nothing fits the unique needs of AI execution at scale.

• Project management tools like Jira and Trello worked well for traditional software but didn’t map well to the iterative, research-heavy nature of AI development.
• Collaboration platforms focused on code-sharing but lacked structured visibility into project evolution.
• Developer training platforms provided education but didn’t integrate learning into real-world execution.

Instead of forcing AI teams to adapt to outdated workflows, we decided to build a platform specifically designed for AI execution, talent development, and transparent collaboration.

What is Omdena’s Human-Centered AI platform?

Our tech team brought together all the learnings and ideas mentioned above and built a human-centered AI platform with the following features:

• Smart Project Execution with Full Transparency

The platform is built to streamline workflows, track progress, and ensure transparency. It includes features like sprint feedback, code quality checks, DevOps monitoring, automated onboarding, progress tracking, and executive oversight, all designed to boost efficiency and allow teams to focus on high-impact work. The platform is excellent for keeping everyone on the same page, ensuring that projects stay on track, and reducing the time spent on routine management. Plus, the AI-driven insights help make more intelligent decisions throughout the project.

• Best Team Formation with a Balanced Skill-Set

Omdena has over 30,000 AI Engineers who have been involved in various projects and have upskilled them. We analyze each piece of code they commit to the project and provide feedback by our code pure AI Agent, thus building a 360 profile for each engineer that holds their pulse from code, interpersonal skills, education, and work ethics. On top of the 360 profile, we built the CRUD app which uses advanced algorithms to match the best-fit talent to a project, not only based on the technical skills but also based on the motivation and willingness to learn. We have, in fact, seen that the latter skills are equally if not more important for the success of a project.

• Continuous Training and Learning

This app helps us to continuously onboard, train, and engage 30,000+ AI professionals, ensuring access to top-tier talent. The engineers also get regular feedback on the code level through AI agents on how to improve their code, especially for real-world applications.

Benefits of Omdena HCAP Scalable and Ethical AI Development

Through the above platform, we solved all our challenges and achieved our AI development ethical framework.

• Transparency and explainability: We make our AI systems as transparent and explainable as possible so that everyone can understand how they work. This helps build trust and rapport with stakeholders.
• Fairness: We consider the potential impact of our AI systems on different groups of people and take steps to mitigate bias. We want our AI systems to be fair and equitable for everyone.
• Continuous monitoring and evaluation: If requested, we can help a client regularly monitor and evaluate the performance of AI systems to ensure that they are being used ethically. This allows us to identify potential problems early on and take steps to address them.
• Compliance with regulations: We ensure that our AI projects comply with all applicable laws and regulations. This helps avoid legal problems and ensures that our AI systems are being used responsibly.
• Education and training: We provide education and training to team members and stakeholders on ethical considerations in AI and data science. We want to ensure that everyone involved in our AI projects understands the ethical issues involved and is committed to using AI in a responsible way.
• Community and collaboration: We encourage collaboration with other organizations and researchers to advance ethical practices in AI and data science. We want to share knowledge and best practices and build a more ethical AI community.

Why is AI PaaS replacing SaaS, and how does Omdena’s platform fit into it?

The AI industry is shifting. Traditional Software-as-a-Service (SaaS) platforms are becoming obsolete, and AI Platform-as-a-Service (PaaS) models are taking over.

We see that the future of AI will be built by IaaS (Infrastructure as a service) and PaaS (Platform as a service) companies. There will also be service providers that will utilize the IaaS and PaaS companies to build custom AI solutions. The days of the SaaS platform are limited. The lack of adaptability and the idea of one size fits all was never a good option for companies, but there was no other option for smaller to medium companies. The cost and time to build custom solutions were way too high. However, in the age of AI, every company can create its own custom SaaS app at a reasonable price and within a short time.         

Omdena can be considered both a PaaS and a service provider company. Other companies can utilize its platform to build their own custom AI solutions using their own engineers but can also be an AI service provider, thanks to the Omdena community and access to the global talent pool.

Why SaaS is No Longer Enough

• Limited customization – SaaS tools force companies into rigid workflows.
• Slow development cycles – Off-the-shelf AI solutions often struggle with adaptability.
• High costs for customization – Tailoring SaaS solutions require expensive modifications.

Why AI PaaS is the Future

• Scalability and flexibility – Businesses can build AI solutions tailored to their needs.
• Faster innovation – Agile development cycles allow for rapid iteration.
• Lower costs – AI models can be developed cost-effectively without vendor lock-in.

Omdena operates at the intersection of PaaS and AI consulting, offering both a powerful AI development platform and a global network of AI experts to execute projects.

How Omdena Compares with Traditional SaaS and Consulting Firms

Omdena's human-centered AI Platform as a Tool for Capacity Building

Beyond AI development, Omdena’s platform is also a capacity-building tool for companies, governments, and organizations looking to train their workforce in AI.

Omdena’s platform is already being used for enterprise AI training, workforce upskilling, and national AI capacity-building initiatives.

Conclusion: The Future of AI is Here

AI is not just another trend—it is a technological revolution. It is going to change fundamentally how we live and work. AI is like an industrial revolution or an incoming new internet age. During the 1990s and early 2000s, everyone had to learn how to use the internet irrespective of their job. We believe the same is and will be with AI. In the next couple of years, everyone will have to learn how to use ‘AI’ and some level of ‘AI engineering’.

Omdena’s?Human-Centered AI Platform?is designed to make this vision a reality—It empowers businesses, engineers, and communities to create AI that is scalable, ethical, and impactful.        

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