Research & White Papers
Architectural frameworks, governance paradigms, and systemic solutions targeting the resolution of probabilistic AI vulnerabilities in enterprise environments.
The Ghost in the Machine: Erasing AI Hallucinations from High-Stakes Business
Examines the architectural root causes of computational errors in enterprise LLMs and introduces the ELIZA framework to achieve zero numerical hallucinations in business forecasting.
The Broken Abacus: Why Corporate America Cannot Trust LLM-Generated Financial Reports
Unpacks the core probability flaws of transformer tokenization and dot-product attention when applied to strict corporate accounting environments.
Beyond Probabilistic Mathematics: Integrating Small Language Models (SLMs) with Deterministic Financial Computing Engines
Details a bifurcated, dual-engine architecture separating context translation from deterministic mathematical registries to guarantee absolute data provenance.
Hallucinated Justice: Large Language Models, Fabricated Legal Citations, and the Future of Deterministic AI in Courtroom Advocacy
Explores structural compliance issues of general-purpose LLMs in law and presents Lexi, a specialized retrieval model matching evidentiary precision.
Large Language Models, Financial Reporting, and Sarbanes–Oxley Compliance: Structural Risks, Regulatory Implications, and Governance Requirements
Establishes the fundamental compliance breakdown when black-box neural networks intersect with audited workflows under SOX Sections 302 and 404.
Investment Thesis: The Rise of Deterministic Financial AI Infrastructure
A macro-level assessment highlighting why venture capital must prioritize governance-aligned, hallucination-free systems over larger language models.
Founder-Facing Opportunity Narrative: Why Now Is the Moment to Build Deterministic, Hallucination-Free Financial AI
Outlines the massive market vacuum waiting for founders building deterministic domain-specific tools rather than general presentation layers.