I provide Architectural Sovereignty for organizations scaling autonomous agent fleets. I bridge the gap between high-level mathematics and production-grade reasoning, engineering verifiable AI systems that optimize token budgets and eliminate agentic fragility.

As an Applied Mathematician (UBA) and AI Architect, I specialize in moving beyond "vibe coding" into principled systems with formal guarantees of stability and cost-efficiency.

CORE SPECIALIZATIONS:
• High-Density Reasoning Loops: Orchestrating hierarchical multi-agent systems using LangGraph and PydanticAI.
• Mathematical Optimization: Applying Path Integrals and Topological Data Analysis (TDA) to bound hallucination probabilities and identify logical redundancy.
• Verifiable Architecture: Implementing verifier-coupled loops to ensure autonomous systems act only when the information budget clears.

ENGAGEMENT TIERS:

The Agentic Blueprint: A 1-week technical audit and formal DAG specification to eliminate architecture risk before a single line of code is written.

The Architect-in-Residence: Full-cycle production hardening, orchestration design, and building a "Topological Moat" that protects your competitive advantage.

I focus on business-critical outcomes: reducing reasoning latency, optimizing Cost-Per-Inference (CPI), and guaranteeing claim-trace consistency for enterprise-scale deployments.