SHU builds private AI infrastructure for organizations that cannot afford data leakage, compliance ambiguity, or external model dependency. This brief covers the founders, the patented architecture, the market, and the current convertible-note round.
SHU was founded by two operators with adjacent backgrounds in regulated infrastructure: classified intelligence and biotech on one side, national-scale healthcare systems on the other.
Built by operators of regulated systems — not by AI generalists.
Engineer, scientist, and serial founder with over 20 years of CEO experience across military intelligence, biotech, and frontier technology. Designed and commercialized the world's first dual-core laptop in partnership with AMD. As founder of Maxwell Biosciences, pioneered plasma proteomics big-data computation and biomimetic drug design, with multiple patents in antiviral applications and human gene expression.
Technical architect of SHU's patented ITI system. Over 20 years in software architecture, systems engineering, and team leadership across biotech, healthcare, and gaming, with a track record of infrastructure that operates at national scale under regulatory pressure. During COVID-19, directed deployment of a mission-critical patient management system across 150+ hospitals while serving as lead engineer on ClinicalTrials.gov and MedicalCountermeasures.gov.
Marshall leads SHU as CEO, bringing operating experience from data-sensitive, compliance-driven industries. As an operator in e-gaming and online betting — sectors where data privacy is non-negotiable and regulatory failure carries direct consequences — he has built the discipline required to take a defensible technology to market. His remit covers commercial strategy, sector prioritization, and the path from patented architecture to product.
The technology behind SHU was originally built to solve a problem inside a biotech business — then opened to organizations facing the same one.
Your business is none of your AI's business.
Scotch McClure spent years building Maxwell Biosciences, a research company with novel antiviral platforms and gene expression technology — decades of work that could not be replicated or recreated if exposed.
Adopting commercial AI in that environment created a clear conflict. Every prompt, dataset, and proprietary insight submitted to a third-party model became part of that model's training surface, permanently and without recall. For a research-driven business, the implicit cost of using off-the-shelf AI was unacceptable.
Rather than absorb that cost, Scotch engaged his brother Jon — the engineer who had operated a 150-hospital infrastructure during a pandemic — to build a system that processed sensitive data without forfeiting ownership of it.
Jon built it from first principles. Not a wrapper, not a fine-tuned model, but a new architecture in which data is processed and structured at the moment of ingestion. The architecture was patented before it left the lab. Maxwell Biosciences has run on it ever since.
SHU is the company that opens that architecture up to other organizations facing the same trade-off.
A summary of the architectural difference between SHU and standard retrieval-augmented LLM products.
Deterministic AI workflows. Outputs you can audit.
Standard AI products perform comprehension at the moment of query. When a user asks a question, the system scans documents in real time, infers context, and assembles an answer from incomplete information. This process is repeated, in full, for every query.
The consequences are predictable: higher token consumption, higher energy cost per query, longer response latency, and elevated hallucination rates — driven by the fact that the model is reasoning and retrieving simultaneously, with no prior structural understanding of the data.
Ingestion-Time Intelligence restructures the workload.
When a document enters SHU, it is read, summarized, entity-mapped, linked to related documents, and tagged with the questions it can answer — before any query is made. By the time a user submits a question, the underlying knowledge structure already exists.
The measurable outcomes are sharper answers, lower hallucination rates, faster response times, and substantially lower compute and energy cost per query. The system holds a persistent, structured representation of the organization's knowledge — permanently and on the customer's terms.
A direct comparison of how data is handled, where the work is done, and what that means for cost, accuracy, and ownership.
Answers drawn from pre-built knowledge maps. No real-time document scanning at query time.
A structured, living map of the organization's knowledge — linked, tagged, available across sessions.
A fraction of the tokens and energy per query relative to standard LLM retrieval architectures.
Data is processed and protected at ingestion. It does not leave the environment and is not used to train external models.
Built from first principles with no dependency on third-party LLM infrastructure. End-to-end sovereign stack.
Jon McClure did not optimize an existing AI architecture. The system was built from first principles, without LLM scaffolding, to address a specific gap: understanding the data corpus before any query is made.
The McClure brothers filed for patent protection at the point the architecture was demonstrated to work.
The ITI patent covers a method of AI data processing in which comprehension, mapping, entity recognition, document linking, and query preparation occur at the moment of data ingestion rather than at the moment of query. It is not an incremental improvement on existing retrieval-augmented systems — it is a structural change to where the comprehension workload sits in the pipeline.
Rapid commercial AI adoption over the past three years has produced a structural exposure that most organizations have not yet quantified.
Your team may already be training external AI systems.
Most organizations adopted AI without fully reckoning with the data exchange involved. Patient records, legal strategies, government intelligence, and proprietary research were submitted to third-party systems in return for analytical capability.
That data does not simply pass through these systems. It is retained, used in training, and absorbed into infrastructure the customer does not own, cannot audit, and cannot retrieve from. This exposure exists before any breach occurs.
The headline breach costs are significant. The figure that warrants more attention, however, is the prevalence of shadow AI — unauthorized AI use inside organizations.
1 in 5 organizations suffered a breach caused specifically by shadow AI: employees using unauthorized tools that no security function had vetted or governed, feeding company data into systems with no oversight.
The implication is that the tools already in use across most enterprises are themselves the primary vulnerability.
The data-handling profile SHU is built for exists across every regulated industry. The exposure is structural, not niche.
Built for regulated systems.
Patient records, clinical trial data, and drug research submitted to AI systems produce direct regulatory and competitive liability. A single breach can expose years of R&D and millions of patient records.
Client privilege, case strategy, and due diligence material are among the most sensitive data classes there are. Using standard AI tools with this material can constitute a professional violation in many jurisdictions.
Trading strategies, client portfolios, and M&A intelligence routed through AI systems that retain, learn from, and can potentially leak market-sensitive information.
Interior ministries, intelligence agencies, and defence contractors cannot route citizen data, classified material, or national security intelligence through third-party infrastructure. The exposure is sovereign rather than purely financial.
The output of any consulting or diligence engagement is built on confidential client information. Submitting that material to a shared AI model is exposure, not analysis; one cross-contamination event can end engagements and firms.
E-gaming, online betting, and digital platforms operate under heavy regulatory scrutiny across multiple jurisdictions. User data, behavioural profiles, and financial transactions require the highest standard of data sovereignty.
Three pillars of a private-AI infrastructure play at the inflection point.
Every SMB and mid-market firm has been quietly handing proprietary know-how to public AI. The regulatory and competitive backlash has begun. Enterprises won't tolerate it. Hosted private AI moves from "nice to have" to mandatory inside the next 18 months.
SHU is positioned at the exact moment this market begins forming — with infrastructure already built and patent-pending, not a slide deck.
Every other player in this space uses RAG. RAG misses the big picture. SHU's Ingestion-Time Intelligence processes context before search — yielding 5×–50× efficiency gains in cost and hardware.
This isn't an optimization. It's a different architecture. Patent pending.
This is infrastructure every major AI player needs but cannot ship — because their business model depends on the data they harvest. That makes SHU acquisition-shaped.
Likely acquirers include Google, Box, OpenAI, Anthropic, XAI, and Glean. The product is already a complete drop-in.
Three tracks. All operational. No vanity spend.
Direct enterprise outreach, channel partnerships, and white-label pilots. Get SHU into the hands of SMBs and mid-market firms ready to leave Big AI.
Strategic conversations with potential acquirers and integration partners. Position SHU in the seat where major tech players come looking — not the other way around.
Extend Ingestion-Time Intelligence, expand the proactive experiences engine, harden white-label deployment, and ship more connectors. Compounding moat work.
SHU's private-by-design architecture is exactly what Big AI cannot build internally — because their economics rely on training data they harvest. We are the only safe shape for them to acquire.