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Company

High-fidelity
Intelligence of Nature
viaHI·AI

HIFAI™ conducts full-stack applied-AI R&D: from the AI tech stack to its integration in business and impact on society, grounded in peer-reviewed research, NVIDIA professional certifications, practical wisdom, and field-proven results.

Interlocking gold gears and chains — the complex reality
We model complex reality
Nested indigo cubes — structured layers
We structure it
Abstract layered lattices of nodes scaling outward — building AI
We build AI for integration
GPU Computing auto-scale chart — capacity tracks demand, 99% uptime held
We autoscale AI & measure AI impact
What “high-fidelity” means

Faithful to the Intelligence of Nature

Fidelity is a word from sound: a high-fidelity system reproduces the original faithfully — nothing added, nothing lost.

The world talks of two intelligences — Human and Artificial. The truest, highest-fidelity intelligence is a third, older than both and too easily forgotten: the Intelligence of Nature — how the real world actually behaves.

Both HI and AI have strengths and limitations, and neither is the Intelligence of Nature itself. Human Intelligence — even the hard-won judgment of an expert who has spent a career studying — comes close to it, but is never the same as it, and is bounded in rationality, scale, and reach. Artificial Intelligence spans vast data, but on its own averages, approximates, and sometimes invents. Their coadaptation — HI◐AI — reproduces the Intelligence of Nature at higher fidelity than either alone: closer to the original, though never the original itself.

HIFAI™ is the facilitator of that creation — helping our human intelligence, coadapting with AI, come ever closer to the high-fidelity Intelligence of Nature it approaches but never becomes.

Why us

Full-stack proven strength

Most AI builders master the tech layers and stop. We go further — into integration and impact, backed by three decades of interdisciplinary research. The AI is only as valuable as the layers above it.

Impact layer

Market · Society · Humanity

A mechanism designer & game theorist — a pioneer since the late 1990s, designing new auctions against mis-identification and price manipulation (false-name and shill bidding): the first manifestations of misinformation, now the defining challenge of the AI era. Co-originated the equilibrium theory of an end-auction “button” during bidding — the analytical foundation behind the “golden buzzer.”

Hidvégi, Wang & Whinston (2006) Buy-Price English Auction, J. Economic Theory.  ·  Wang & Skovira, (2017) Authenticity and Social Media, Proc. 23rd AMCIS.
Integration layer

Business · Industry

A business-school IS professor — Emory (Goizueta), UNLV, Trident, and Robert Morris — with three decades in academia and nearly 100 refereed publications in technology adoption, management, and information systems. Her doctoral students now serve at MITRE, the U.S. State Department, and DHS. PhD, UT Austin; advisor Andrew B. Whinston.

Knaggs, Pollard & Wang (2012) Applying Theory of Constraints in administrative process: an experiment from the U.S. government, Proc. 3rd ICMSE.
AI tech layer

Huang’s five-layer AI cake

Energy · chips · infrastructure · models · applications — across the full stack, not just the top: building at the infrastructure, model, and application layers, on GPUs, CUDA, cloud/K8s, and NVIDIA blueprints. Four NVIDIA certifications: Professional in Agentic AI, Gen AI LLMs, and Accelerated Data Science; Associate in AI Infrastructure & Operations — plus 35 course certificates, spanning networking, LLMs, RAG agents, multimodal, and MLOps.

Wang & Murphy. Nuclear-powered AI data centers: Cybersecurity and governance framework, IIS (accepted).
Wenli Wang PhD
Founder & CEO

Wenli Wang PhD

Dr. Wang holds the highest faculty distinction — University Professorship — at Robert Morris University, with earlier faculty appointments at Emory University’s Goizueta Business School and other universities over the years. With 30 years of information-systems and multidisciplinary research and nearly 100 publications, she brings rare depth to mindful technology adoption and systems engineering.

Dr. Wang was the first in the world to co-introduce the “button” mechanism to end a dynamic auction (published in Journal of Economic Theory) — laying the theoretical groundwork for the “golden buzzer” later popularized on America’s Got Talent. As a game theorist, mechanism designer, and computer scientist, she has spent her career studying how to design and implement computerized systems that maximize performance — considering all actors’ reasoning under uncertainty. She is a disciple of Dr. Andrew B. Whinston at the University of Texas at Austin — recipient of the AIS LEO Award for lifetime achievement in Information Systems, and himself a professor across economics, computer science, and information systems. He is the root of her own multidisciplinary breadth and depth. It is the intellectual foundation of how she advises on AI today: knowing where AI fits, where it does not, and how to measure the difference.

Dr. Wang is as hands-on as she is theoretical — an NVIDIA Certified Professional in Agentic AI, Gen AI LLMs, and Accelerated Data Science, and a Certified Associate in AI Infrastructure and Operations — building the AI she designs from the systems engineering side, not just the strategy behind it.

Her current AI work spans some of the most demanding fields: surgical robotics AI and clinical decision-making at hospitals, cybersecurity at large international firms, physical security innovations, and customer relationship management at platform companies.

Through HIFAI™, she helps organizations adopt AI wisely — and build custom AI where off-the-shelf models can’t suite their fields — amplifying cleint's decades of hard-won human intelligence and experiences into even stronger defensible competitive advantages.

“Coadapt. Create. Coalesce.”

Hands-on and current — NVIDIA-certified across the stack she builds on.

Delivery network

Senior hands, scaled to the work

When a build needs to scale big, a vetted network of senior AI engineers with big-tech-caliber will be mobilized — with backgrounds spanning clinical and hospital AI, federal and government systems, and more, engaged per project. Senior people on the build, scaled to the work, without standing overhead.

In good company

Distinguished perspectives

Rear Admiral Norman R. Hayes, U.S. Navy (Ret.)
Norman R. Hayes
Rear Admiral, U.S. Navy (Ret.)

A career Naval Intelligence Officer with three decades of strategic and operational intelligence — across theater, cyber, and maritime operations — at the highest levels of U.S. national and international agencies.

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