Juan Segura [Founder]

The Compound Error Problem

This realization led to a deeper insight: when you chain together multiple reasoning steps, even small suboptimal decisions compound dramatically. If each step in a business process has a 3-5% chance of producing a non-optimal result, then by the time you've executed 20, 30, or 50 steps—the kind of goal decomposition required for real business processes the cumulative error rate makes the final outcome unreliable.

Business processes aren't one-shot questions. They're complex webs of strategic goals that decompose into tactical goals, then operational goals, then specific executable steps. Each level involves ambiguities, crossroads where you must choose the next direction, decisions about how to interpret information and verify it before moving forward. Placing all of this inside an LLM which operates through statistical prediction almost guarantees suboptimal results.

A Different Architectural Philosophy

This led me to a fundamental insight: we should use LLMs for what they excel at their remarkable knowledge base, their ability to interpret and synthesize but as tools within a broader reasoning system, not as the reasoning system itself.

When companies like OpenAI introduced "thinking tokens" built directly into the LLM infrastructure, my intuitive reaction was that this was the wrong path. Instead of trying to inject reasoning into the predictive framework, I wanted to build something that mimicked human reasoning outside the LLM using the language model as a sophisticated tool within a larger orchestration system.

Vinciness: Reasoning Beyond Prediction

This vision became Vinciness a system designed to achieve what I call business AGI through systematic goal decomposition and autonomous execution. Rather than relying on statistical prediction for complex reasoning chains, Vinciness operates through planning engines, execution engines, and reasoning engines that work together, using LLMs strategically rather than depending on them entirely.

The system doesn't start with a prompt it starts with a strategic goal, then systematically decomposes that goal into tactical objectives, those into operational goals, and those into executable steps. Only by maintaining this hierarchical structure and ensuring optimal results at each level can we build chains long enough and reliable enough to handle real business processes autonomously.

The Information Challenge

Another critical insight emerged: to truly understand complex problems, you need access to vast amounts of nuanced, high-quality information not just the most statistically probable responses. Business AGI requires working with edge cases, niche domain knowledge, and specific contextual information that generalist models struggle to access or prioritize correctly.

Traditional LLM reasoning is constrained by inference costs and context windows. Even as these limitations ease, the fundamental challenge remains: the more information you feed into a context window, the more the system gravitates toward generalized responses rather than the specific, nuanced understanding that complex problems demand.

Current Frontiers: Ambiguity and Knowledge Synthesis

Vinciness solves 63.3% of GAIA Level 3 problems and manages to solve Humanity's Last Exam problems that GPT-5 Pro can't solve.

Yet significant problems remain unsolved by both systems across these benchmarks, revealing the fundamental challenges separating current AI from human-level reasoning.

Some of these benchmarks deliberately exploit AI weaknesses. GAIA injects "thought viruses" carefully crafted ambiguities designed to derail language model reasoning. Consider the word "significant" in conversation. Humans instantly understand which contextual meaning applies: statistical significance in research, importance in business decisions, substantial quantity in financial contexts, or personal meaning in emotional situations.

For AI systems, this same word presents multiple possible interpretations without clear contextual resolution statistical patterns can't replicate the intuitive understanding that humans develop through lived experience in specific domains.

But beyond handling ambiguous language lies an even deeper challenge: how intelligence processes and synthesizes knowledge. A pediatrician with twenty years of practice diagnoses through accumulated intuition, drawing on compressed knowledge from countless medical textbooks and cases, now synthesized into rapid pattern recognition. They're not consciously accessing external references because the knowledge has become internalized expertise.

Current AI systems, including Vinciness, operate fundamentally differently through brute force information processing. We load selected raw data into context windows and process it computationally. The context window limitation isn't really about storage capacity it's the absence of knowledge compression that allows intuitive shortcuts.

Whether AI can develop architectures that synthesize information into compressed expertise remains the central challenge toward artificial general intelligence. The insight isn't gaming benchmarks but understanding what they reveal about the architecture of intelligence itself.

Synthesis and Continuous Learning

Perhaps most importantly, Vinciness is designed around the principle that Leonardo da Vinci captured perfectly: "Learning never exhausts the mind." Unlike systems that treat each problem in isolation, Vinciness tries to builds persistent knowledge, synthesizing insights from vast information sets and growing more capable with each engagement.

This isn't just about scaling up it's about creating systems that can draw genuine learnings from information, building understanding that compounds over time rather than starting fresh with each query.

The Transformation Ahead

My vision is straightforward but ambitious: if we can create systems that spend seven hours accomplishing what would take humans 80 or 90 hours doing it flawlessly, correctly, in depth, without fatigue we will transform everything we do. The biggest cost in almost every organization is human time and effort. Tools that can work tirelessly alongside us won't just improve efficiency; they'll free us to focus on what humans do best: creativity, innovation, and strategic thinking.

This isn't about replacing human intelligence it's about amplifying it. When reasoning systems can handle complex execution while maintaining optimal results across long chains of decisions, they become genuine partners in solving the problems that matter most to us.

A Small Team, A Large Vision

Vinciness exists today as a working system, developed by our small but dedicated team who share this vision of reasoning beyond prediction. We're building toward a future where autonomous systems can handle the full complexity of business processes not through bigger hammers, but through better toolboxes.

The pursuit of business AGI will be exhaustive, as da Vinci might have said, but it's a journey that promises to transform how we think, work, and solve problems. In a world where learning truly never exhausts the mind neither human nor artificial the possibilities become limitless.

Founder

Juan Segura