Simon Markus Putz

Designing production-grade distributed architectures and deterministic TypeScript validation engines for LLM pipelines.

Executive Summary

My career as a system architect focuses on engineering structural predictability into complex backend systems. I entered the technology sector over a decade ago, specializing in large-scale data indexing, semantic taxonomy, and structural web engineering. With the emergence of Large Language Models (LLMs), I pivoted my core focus toward resolving the systemic non-determinism of generative AI, engineering solutions for state-management failures, downstream data corruption, and auditability gaps.

As the Founder and Chief Architect at Stratona Global, I architect deterministic, post-processing validation layers. These microservices sit downstream from LLMs, executing real-time schema enforcement and causal validation for institutional financial pipelines, corporate legal workflows, and enterprise environments.

Core Architecture: The Deterministic TypeScript Validation Pipeline

Stratona’s production infrastructure isolates unreliably generated outputs from core data layers through a strict, asynchronous Parse-Validate-Refine lifecycle:

• The Validation Layer (TypeScript & Node.js): The entire evaluation engine is written in strictly typed TypeScript. Leveraging the asynchronous, event-driven nature of the V8 runtime, the system parses LLM payloads natively into strict type definitions, eliminating processing latency and ensuring memory-safe data evaluation.
• The Graph Engine (Causal Hypergraph Model): To validate multidimensional financial and legal datasets, the pipeline injects payloads into a proprietary Causal Hypergraph infrastructure. Unlike standard binary relational databases, this hypergraph maps directed, n-äre causal dependencies across multiple records simultaneously. The system matches the unstructured LLM output against this mathematical constraint matrix in real-time.
• The Error-Correction Loop (Durable Execution): If a schema violation or causal anomaly is detected, the process transitions into an automated correction loop. Managed by a Durable Execution Engine, the entire transaction state is persisted. If an API timeout or node failure occurs, the state machine resumes execution mid-loop, guaranteeing fault-tolerant Re-Prompting cycles until the dataset strictly complies with the target schema.

Methodological Focus & Complex System Debugging

My architectural methodology relies on highly analytical pattern recognition, driven by Autism Spectrum Disorder (ASD). Where traditional development teams approach debugging sequentially, my cognitive framework processes multi-dimensional, concurrent data states simultaneously. This non-linear processing asset allows me to rapidly isolate race conditions, model complex edge cases, and reverse-engineer structural anomalies in high-throughput distributed systems.

Intellectual Property & Enterprise Due Diligence

Our core methodology for post-processing graph validation and state-persisted error recovery is currently protected via a pending Austrian Patent. Full system documentation, TypeScript API definitions, and architectural blueprints are open for technical review during standard corporate Technical Due Diligence cycles under standard non-disclosure agreements (NDA). Stratona Global’s validation engines are currently scaled to automate, audit, and systematically secure next-generation corporate financial reporting.