Category Thesis
Today's AI predicts what is likely. XIXUM reasons from what is valid — it structures meaning, rules and context before an answer or action is ever used.
From chat into agents, workflows and autonomous enterprise execution.
AI can sound right while being wrong, inconsistent or unsupported by context.
XIXUM structures meaning, rules and context before a result is trusted.
The Problem
LLMs, RAG pipelines, agents and workflow tools can generate, retrieve, recommend and trigger actions. But they do not reliably validate whether a result is meaningful, consistent or safe to use. When AI only writes, a hallucination is content risk. When AI acts, it becomes business, compliance and safety risk.
Plausible output is not operational trust.
The Solution · ReasoningOS
Today's AI
Most systems learn patterns from data and generate plausible outputs. Context is a token window; correctness is a hope.
XIXUM
XIXUM starts with meaning, rules, constraints and context — a semantic meaning space, not a token window — then determines whether a result is valid before it is used.
Meaning, made explicit
Terms, assumptions, rules and constraints are made explicit — not buried inside fluent text.
Consistency
If something does not fit, XIXUM does not simply continue generating — the conflict becomes a visible model state.
No invention
Where validity can't be established, it asks for correction or flags the gap — rather than filling it with a fabrication.
XIXUM is not a hallucination guardrail. It is AI designed around verified meaning.
The Mechanism
Derives conclusions from principles and causal chains within a context — instead of reproducing probable patterns.
Terms, rules and constraints are made explicit and checked for consistency before any answer or action is used.
Contradiction or missing context becomes a visible model state — not something hidden behind fluent text.
Where validity cannot be established, the system asks back or flags open, rather than inventing an answer.
The validation loop
Architecture shown at concept level. The underlying mechanism is reserved for technical due diligence under NDA.
The Opportunity
Outputs that must be traceable, explainable and reviewable.
Investment, risk and capital decisions where unsupported assumptions create exposure.
Orders, invoices, approvals and supply-chain decisions where inconsistency creates cost.
Safety-critical and sector-specific workflows where evidence and accountability matter.
Competitive framing
Probabilistic AI is the acceleration layer. XIXUM is the validation layer.
Substance · Proof
German federal R&D certification. Application in final review; technical depth substantiated in a detailed appeal — independent scrutiny of genuine R&D.
ReasoningOS preview running on a node-graph context model with live surfacing. Pre-MVP, self-funded, own IP.
Publications incl. Springer Nature (under review), AIP Conference Proceedings, IOS Press / Fraunhofer.
15+ years in regulated, safety-critical software: ISO 26262, SOTIF, DO-178C — the discipline behind verifiable systems.
Investment Thesis
The company that makes AI reliable enough to act owns the next trust category.
Direct
Felix Schaller — Founder & CEO