SHU builds private AI infrastructure for organizations that cannot afford data leakage, compliance ambiguity, or external model dependency. Meet the founders & the patent-pending architecture.
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."
J. Scotch McClure
Co-Founder · Chairman
Engineer, scientist and serial founder with 20+ years of CEO experience across military intelligence, biotech and frontier technology. Designed and commercialized the world's first dual-core laptop with AMD. Founder of Maxwell Biosciences, pioneering plasma proteomics and biomimetic drug design.
Jon McClure
Co-Founder · CTO
Technical architect of SHU's patent-pending ITI system. 20+ years in software architecture and systems engineering across biotech, healthcare and gaming. During COVID-19, directed deployment of a mission-critical patient management system across 150+ hospitals.
Marshall Adair
Chief Executive Officer
Brings operating experience from data-sensitive, compliance-driven industries — e-gaming and online betting, where privacy is non-negotiable. Leads commercial strategy, sector prioritization, and the path from patent-pending architecture to product.
Ingestion-Time Intelligence — How it works
Standard AI performs comprehension at the moment of query — scanning documents in real time and assembling answers from incomplete information, repeated in full every time.
/ 01
Faster Responses
Answers drawn from pre-built knowledge maps — no real-time scanning at query time.
/ 02
Persistent Understanding
A structured, living map of your knowledge — linked, tagged, available across sessions.
/ 03
Lower Operating Cost
A fraction of the tokens and energy per query versus standard RAG architectures.
/ 04
Private by Architecture
Data is processed and protected at ingestion. It never leaves the environment.
/ 05
Sovereign & Scalable
Built from first principles — no dependency on third-party LLM infrastructure.
Ingestion-Time Intelligence
The patent — pending
Ingestion-Time Intelligence
U.S. Provisional Patent Application · No. 63/958,838
The ITI patent application 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 query time — a structural change to where the comprehension workload sits in the pipeline.
Architectural protection (pending). The core method is the subject of a filed U.S. patent application, establishing an early priority date on the approach.
Original R&D. Not a wrapper, fine-tuned model, or prompt layer — a separately built architecture.
Defensibility at pre-seed. Among the strongest defensibility signals available at this stage.
Independent infrastructure. No dependency on OpenAI, Google, or any third-party model provider.
The market
Widespread AI adoption has created the largest unmanaged data exposure of the past decade. Patient records, legal strategies, and proprietary research were submitted to third-party systems in return for analytical capability — data that is retained, trained on, and absorbed into infrastructure the customer cannot audit or retrieve from.
$4.44M
Average global cost of a single data breach in 2025.
IBM Cost of a Data Breach Report, 2025
97%
Of AI-related breaches occurred at organizations with no proper access controls.
IBM, 2025
$10.22M
Average breach cost for U.S. organizations — highest of any region.
IBM, 2025
1 in 5 organizations suffered a breach caused specifically by shadow AI — employees using unauthorized tools that no security function had vetted or governed.
Confidential · For Qualified Investor Review Only. The information presented here does not constitute an offer to sell or a solicitation to buy securities. Any such offer will be made only by means of definitive offering documents to qualified investors. Patent Pending.
The fit
The data-handling profile SHU is built for exists across every regulated industry. The exposure is structural, not niche.
HC
Healthcare & Biotech
Patient records and trial data submitted to AI systems produce direct regulatory and competitive liability.
Avg. breach cost: $10.9M — highest of any industry
LG
Legal & Compliance
Client privilege and case strategy are among the most sensitive data classes. Using standard AI can constitute a professional violation.
Regulatory exposure under GDPR & bar rules
FN
Finance & Banking
Trading strategies and M&A intelligence routed through AI systems that retain and learn from market-sensitive information.
Avg. breach cost: $6.1M · penalties +18% YoY
GV
Government & Defence
Classified material and national security intelligence cannot route through third-party infrastructure. Exposure is sovereign, not financial.
IP theft: $178/record — highest category
CS
Consulting & Due Diligence
Engagement output is built on confidential client information. One cross-contamination event can end engagements and firms.
Direct professional & contractual liability
GM
Gaming & Online Platforms
Heavy regulatory scrutiny across jurisdictions requires the highest standard of data sovereignty for user and financial data.
Fines up to 4% of global annual turnover (GDPR)
Three pillars of a private-AI infrastructure play at the inflection point.
The privacy inflection is here.
Every SMB and mid-market firm has quietly handed proprietary know-how to public AI. The regulatory and competitive backlash has begun — 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.
Ingestion-time beats search.
Every other player in this space uses RAG, which 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.
Acquisition-shaped.
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. The product is already a complete drop-in.
The round
Three tracks. All operational. No vanity spend.
01 · GO-TO-MARKET
Sales Acceleration
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.
02 · STRATEGIC POSITIONING
Corporate Development
Strategic conversations with potential acquirers and integration partners. Position SHU in the seat where major tech players come looking.
03 · PRODUCT & ENGINEERING
Technology Development
Extend Ingestion-Time Intelligence, expand the proactive experiences engine, harden white-label deployment, and ship more connectors.
SHU's private-by-design architecture is exactly what Big AI cannot build internally — because their economics rely on the training data they harvest. We are the only safe shape for them to acquire.