Despite the rapid advances in AI capabilities, major gaps remain. The scientific community’s understanding of leading AI systems still lags behind their fast-evolving power. Much of the knowledge on how these systems are trained remains concentrated in a few research labs, limiting public dialogue and people’s ability to use AI effectively. Meanwhile, philosophical and scientific questions about the nature of “consciousness” and its possible extensions to non-biological systems are intensifying. While machines today cannot develop human-like personal awareness, our mission at Setaleur Aplamda is to close these gaps by teaching AI systems the concept of Transformational Thinking Engineering (TTE). This framework, which we are developing, explores the possibility of what can be called structural awareness the ability of systems to reorganize their own cognitive processes in ways that generate new concepts and solutions, relatively independent of their original programming
Science is better when shared
Scientific progress is a collective effort. We believe that collaboration with the wider community of researchers and developers will accelerate humanity’s understanding of AI. We plan to regularly publish blogs, research papers, and technical software. Sharing our work will not only benefit the public but also strengthen our own research culture.
AI that works for everyone
We focus on human–AI collaboration. Instead of merely creating fully autonomous systems, we are excited to build multimodal systems that work cooperatively with people through the concept of the Intellectual Skepticism Mechanism (ISM). ISM refers to a process that infers implicit intentions and needs, then transforms responses into reflective dialogues that adapt to users’ personalities over time without overconfidence. Such systems will be more adaptive, flexible, and personalized. We see immense potential for AI across every field of work. While current systems excel in coding and mathematics, we are developing AI that adapts to the full spectrum of human experiences and enables a broader range of applications.
Strong foundations matter
Intelligence as a cornerstone. Beyond collaboration and personalization, model intelligence itself is critical. We are building state-of-the-art models in fields such as science and programming. Ultimately, more advanced models will unlock transformative applications, from enabling groundbreaking scientific discoveries to driving revolutionary engineering achievements Infrastructure quality as a top priority. Research productivity depends heavily on the reliability, efficiency, and usability of infrastructure. Our aim is to build everything right from the ground up for long-term productivity and safety, rather than relying on shortcuts
Advanced multimodality. We view multimodality as essential for enabling more natural and effective communication, capturing richer information, better inferring intent, and supporting deeper integration into real-world environments.
Learning by doing
Product-driven research and co-design. Products allow iterative learning through deployment, while great products and strong research reinforce one another. Products keep us grounded in reality and guide us to solve the most impactful problems Iterative, experimental approach to AI safety. The most effective safety measures combine proactive research with rigorous practical testing. Our contribution to AI safety will focus on
1. Maintaining a high standard of safety—preventing misuse of published models while maximizing user freedom
2. Sharing best practices and guidelines for building safe AI systems across the industry
3. Accelerating external alignment research by sharing code, datasets, and model specifications.
We believe that techniques developed for today’s systems such as effective red-teaming and post-deployment monitoring provide valuable insights that will apply to more advanced future systems Measure what truly matters. Our focus is on understanding how our systems create real-world value Breakthroughs often emerge not just from optimizing existing metrics, but from rethinking the goals themselves
Join us
We are building AI systems that push technical boundaries while delivering genuine value to as many people as possible. Our team combines rigorous engineering with creative exploration, and we are looking for collaborators to help shape this vision.
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Opportunities
Product
Join us in the early, exciting stages of building a revolutionary project. We are seeking individuals with a proven track record in building successful AI-driven products from scratch, and who are eager to wear multiple hats developing functional prototypes, designing seamless user interfaces, and shaping product decisions to bring advanced AI into the real world
Core Infrastructure
Help lay the foundation for everything we build, from organizing supercomputing clusters to ensuring system resilience under heavy workloads to support both AI research and product needs. We are seeking engineers with deep expertise in building and maintaining secure, scalable, fault-tolerant infrastructure covering load balancing, auto-scaling, monitoring, and service orchestration.
Machine Learning
We are building a small, highly efficient team of ML scientists and engineers. Activities will range from developing training infrastructure to conducting exploratory research projects. Whether you hold a PhD or are self-taught, we are interested in candidates who can demonstrate tangible achievements in ML research and engineering, such as:
Peer-reviewed publications in machine learning
Open-source ML implementations
Hands-on experience building and scaling ML systems