Unisapience.ai brings rigorous mathematical frameworks and enterprise-grade consulting practice together to give clients the one thing they can't get from model providers: independent, adversarial, court-defensible AI risk intelligence.
Enterprise AI adoption has outpaced governance infrastructure by years. Boards are approving deployments they don't have the tools to evaluate. M&A deals close on AI-heavy targets with no systematic way to quantify what's been acquired — or what liability comes with it.
Existing approaches — vendor-supplied model cards, point-in-time audits, and generic compliance checklists — share a structural flaw: none of them produce a reproducible, weighted, cross-system score that changes with the model. They describe. They don't measure.
The Shadow Simplex Score (SSS) is built to fill that gap. It treats AI risk the way credit bureaus treat financial risk: as a quantifiable, auditable property of the entity under examination, not a narrative opinion.
EU AI Act Article 9 requires high-risk system operators to maintain "adequate risk management systems." NIST AI RMF and SEC cybersecurity disclosure rules are creating parallel obligations in the US. Boards need defensible documentation, not aspirational policies.
AI systems now appear as material assets or risks in a growing share of transactions. A target's custom LLM stack, training data provenance, and shadow model inventory can each move deal valuation by 15–30%. Standard technical DD misses all of it.
Studies consistently show 40–60% of enterprise AI usage is outside IT visibility. Our Shadow AI Index (SAI dimension in the SSPLX-001 baseline; surfaced through the Group VII × P5 matrix position in the SSPLX-002 extended composite) is specifically designed to surface, catalog, and risk-score these unsanctioned deployments before they become incidents.
A formal mathematical model developed through original research and published as a public pre-print. The framework serves as the intellectual backbone for every SSS assessment.
Failure modes don't occur in isolation. Each of the ten pairwise couplings represents a superadditive interaction — where two active failure modes produce risk greater than their sum. For example:
Each triangular face represents a stable attractor state — a pattern your system can fall into and stay in. Named attractors include:
Timothy brings two decades of enterprise systems architecture across fintech, regtech, and data infrastructure — including tenure as a senior integration architect at Equifax, where he holds a named patent on distributed identity resolution systems (US 10,581,825).
His transition to AI risk consulting grew from a direct observation: the organizations best positioned to exploit AI were being given almost no rigorous instrument to understand what they were actually taking on. Boards were approving deployments they couldn’t evaluate. Deal teams were closing transactions with material AI assets they couldn’t quantify. The Shadow Simplex framework was built to close that gap — not as abstract theory, but as a practical, court-ready instrument for the people who have to live with the consequences.
Timothy's analytical style draws on topology, reinforcement learning theory, and practical enterprise governance experience. He is an active researcher, a named inventor, and a practitioner of pentarchic analysis — the five-aspect decomposition method underlying the Shadow Simplex's 4-simplex structure.
He serves clients in regulated industries across the Southeast US and nationally, with particular expertise in fintech, regtech, and healthcare AI deployments.
We have no financial relationship with any AI model provider. Our assessments cannot be purchased into a favorable outcome.
Qualitative judgments are documented and auditable. Every SSS score is reproducible from its inputs.
The Shadow Simplex framework is publicly available. We believe the field benefits from scrutiny, replication, and critique.
We work for buyers, boards, and GRC officers — not vendors. Our recommendations are made in client interest, period.
AI governance is infrastructure. We design systems that remain defensible as models, regulations, and threat landscapes evolve.
Start with a 30-minute scoping call. We'll tell you exactly which SSS dimensions are most relevant to your situation and what an engagement would entail.
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