Understanding the Technologies Behind Agentic AI Infrastructure
We work as a Manufacturer's Representative / Referral Partner for three specialist companies addressing different layers of the Agentic AI Security Gap. This page explains what each does, why it matters, and how to reach them — not to sell you anything ourselves, but to help you understand the landscape clearly. We plan to add additional companies and to continue to build out a broader ecosystem of companies that can contribute to expanding Defense-In-Depth secure enterprise IT infrastructures.
Trusted Advisor, Not a Reseller
- We operate as a Manufacturer's Representative / Referral Partner — a standard, established role in IT infrastructure sales
- We have no authority to quote pricing or make commitments on behalf of any vendor
- We are paid a referral fee only after a sale closes and the vendor has been paid — drawn from the vendor's own marketing budget, not added to what you pay
- Our job is to help you understand the landscape and make the right introduction when it fits — nothing more
Focus Area 1: Agentic AI Enterprise IT Infrastructure
SourceCode: The Infrastructure Behind the Infrastructure
Every AI strategy eventually runs into a hardware question: who actually builds, configures, tests, and ships the GPUs and servers your architecture depends on? That's SourceCode's role.
Who they are
Founded in 1992 and headquartered in Norwood, Massachusetts, SourceCode is a global co-design and infrastructure partner with engineering and production facilities across the US, UK, and beyond. Acquired by Cerberus Capital Management in 2021 to accelerate its global build-out, and expanded further in 2023 with the acquisition of UK-based Boston Limited.
Why it matters
SourceCode is ISO 9001:2015 certified, and Comark (a SourceCode company) is an ITAR-compliant registered manufacturer — directly relevant given how much CUI- and ITAR-controlled research data runs through university and government HPC environments. Their build process includes an in-house, U.S.-based environmental testing lab and multi-stage hand inspection before systems ship.
Proof point
Vanderbilt University's GPUs, servers, and Hammerspace deployment were all supplied through SourceCode.
Focus Area 2: AI Data Platforms
Hammerspace: Making Your Data Available Wherever Your GPUs Are
The problem Hammerspace solves is more fundamental than most people realize: most enterprises stitch together 15 or more disconnected tools just to prepare data for AI consumption, and roughly 80% of AI project spend happens before a single dollar of ROI shows up.
Who they are
Hammerspace built a "data-in-place" architecture — a global namespace that unifies data across storage systems, clouds, and regions without copying it, so GPU-intensive AI applications can pull data from wherever it actually lives. This includes making previously idle, expensive NVMe SSDs usable by other AI workloads instead of sitting empty during non-training phases.
Why it matters
In March 2026, Hammerspace launched its AI Data Platform (AIDP) at NVIDIA GTC San Jose, built on an NVIDIA reference design, with Secuvy's Data Security Posture Management embedded natively.
Proof point
Vanderbilt University's Advanced Computing Center for Research and Education (ACCRE) selected Hammerspace in November 2025 to unify a 10-petabyte research environment (~750 compute nodes, 80 GPU nodes), replacing three disconnected storage systems (Panasas, GPFS, LStore) with one platform — achieving a verified 48% reduction in average storage cost.
For more information, Request the full case study at the MikeGluck.ai Education Hub — HPC Architecture Patterns.
Featured Resource
The SSD Supply Crisis of 2026: A Strategic Infrastructure Survival Guide
The SSD supply crisis of 2026 reflects a structural reallocation of global semiconductor capacity toward AI infrastructure. To drive AI initiatives and other critical business initiatives, adopting a flexible "open" data architecture mitigates supply chain risk. This Survival Guide outlines the structural drivers behind the supply crisis and provides a practical checklist to reduce supply risk with Hammerspace.
Request the Survival GuideFocus Area 3: AI Data Security and Governance
Secuvy: Governance That Understands What It's Protecting
Most AI security tools rely on RegEx pattern-matching to find sensitive data — which misses a significant share of real research and enterprise data that doesn't fit a standard template.
Who they are
Secuvy's Data Security Posture Management (DSPM) platform is built specifically to handle non-patterned sensitive data — the reason Hammerspace selected Secuvy as its embedded security layer for AIDP. Secuvy's CEO Mike Seashols has described the goal as making security and governance native to the data platform itself, rather than bolted on afterward — calling it a "game-changer for enterprises deploying AI in regulated industries."
Why it matters
In June 2026, the OWASP GenAI Security Project's "State of Agentic AI Security and Governance v2.01" report confirmed that AI safety and security are converging at the deployment layer — and that most governance frameworks were never built for systems that act on their own. One line worth sitting with: authorized access does not automatically mean appropriate AI use.
Case study
A premier U.S. defense contractor running dozens of parallel programs — for NASA, Space Force, the DoD, and the intelligence community — had a governance policy that was unambiguous: CUI, ITAR, EAR, and NARA data must be identified, classified, tagged, and labeled. Secuvy automated discovery found that 38% of files were misclassified per ITAR, CUI & Sensitive PII guidance. In fact, only 1 in 20,000 export controlled files carried the correct sensitivity label.
Request the full case study at the MikeGluck.ai Education Hub — Data Governance & PolicyCase study
BioPharma: From Ungovernable Sprawl to AI-Ready Data
$30M
in residual risk quantified — used to defend cyber-insurance premiums
~40%
lower cyber-insurance premiums achieved
60+ vendors
brought under governed control with <1 FTE
A commercial-stage dermatology biopharmaceutical company relies on third parties to run its clinical trials. When data returns, it sprawls instantly — accessed, copied, and reused by research, BI, and data-science teams. What sprawls is not only patient data but the company's crown-jewel IP: drug formulations, product-development data, target and mechanism research, and patient-population and biomarker insights. Clinical-trial data and IP are largely non-patterned — they don't match the fixed rules traditional tools depend on. Secuvy deployed agentlessly across the hybrid, global estate and classified millions of records in context, including relationship-driven questions like minors and consent expiry that pattern-based tools miss entirely.
Request the full case study at the MikeGluck.ai Education Hub — Data Governance & PolicyFocus Areas 4 & 5: HPC Research and the Education Hub
Two More Pieces, Housed on Our Dedicated Resource Site
Two of our five focus areas — AI for University HPC Research and the Agentic AI Education Hub — aren't vendor partnerships, so rather than duplicate that content here, we've built a dedicated site for them:
How to Read the Claims on This Page
The vendor descriptions, statistics, and case study outcomes above (including Hammerspace's 48% storage cost reduction at Vanderbilt) are drawn from each vendor's own published announcements as of the date cited, and are not guarantees of results for any other organization. Full trademark attributions, our informational-purpose disclaimer, and data handling practices are detailed on our Legal & Privacy page. Legal & Privacy page.
Not Sure Which Piece Applies to You?
Send us your scenario and we'll help you understand which of these — infrastructure, data platform, or governance — is your actual starting point.