Private & Local AI Advisory — Hackademic Solutions

Why Run AI Privately?

Not every organization can put its data in a public AI service. When the data is the risk, the model should run where you control it.

Data Sovereignty & Privacy

Keep sensitive data in-house. Nothing leaves the environment you control — no third-party retention, no training on your data.

Regulatory & Contractual

Meet obligations — HIPAA, CJIS, ITAR, banking rules, client data clauses — that bar sending data to public AI services.

Air-Gap & Maritime

AI that works in disconnected, restricted, and operational environments — ships, defense, and OT networks with no reliable cloud path.

Cost & IP Control

Avoid per-token cloud costs at scale and protect proprietary models, prompts, and fine-tuning data as company IP.

What We Deliver

Advisory and architecture. We design the secure deployment and guide it; your team or integration partner builds. Every recommendation is mapped to the same frameworks we use across every engagement.

Model Selection & Fit

A recommendation on which open-weight or self-hosted models fit your use cases, data sensitivity, latency needs, and hardware budget — with the trade-offs made explicit so the choice is defensible, not vendor-led.

Data & Access Controls

A reference architecture for how data reaches the model and who can use it — identity and access boundaries, retrieval and RAG data flows, logging, and the controls that keep sensitive data inside your perimeter.

Hardening & Guardrails

Deployment hardening, prompt-safety guardrails, and abuse and exfiltration controls mapped to the OWASP LLM Top 10 and MITRE ATLAS — so a private model is not a soft target on your own network.

Evaluation & Acceptance Plan

A practical plan to measure whether the deployment is safe and good enough to trust — quality, safety, and regression checks your team can run before and after go-live, with documented acceptance criteria.

How an Engagement Works

Scope, architecture, and a build-ready plan — vendor-neutral, framework-driven, with human judgment at every step.

1. Scope & Drivers

We start with why private — sovereignty, a specific regulation or contract, an air-gap requirement, or cost and IP control — and the use cases in scope. Your drivers set the constraints the architecture has to satisfy.

2. Architecture & Model Recommendation

We design the secure deployment — model selection, hosting topology (on-prem, private cloud, or edge), data flows, identity and access controls, and hardening — documented as a reference architecture your team can build against.

3. Build-Ready Plan & Handoff

You receive a prioritized implementation plan, an evaluation and acceptance plan, and a controls checklist mapped to NIST AI RMF, ISO 42001, and the OWASP LLM Top 10. Your team or integration partner builds; we guide and review.

Scope, stated plainly: this is advisory and architecture. We architect and guide the deployment; your team or integration partner does the build. Your data, your stack, your control.

Not Sure a Private LLM Is the Right Move?

Start with the AI Posture Review. In 10 business days it tells you whether keeping AI in-house fits your data-sovereignty and regulatory obligations — and what running one safely actually requires. Then this advisory turns that answer into a secure architecture.

Explore the AI Posture Review

Scholar-Practitioner. Vendor-Neutral.

Led by Chris Simpson — PhD Cybersecurity, Navy Lieutenant Commander, TOPGUN Instructor, CISSP, GPEN — with Dr. James Jaurez, Chief Research Officer, whose research spans Explainable AI, human-led systems, and non-human identity architecture. 20+ years of defense and security operations applied through a research-first methodology.

We don't sell you a model or a platform. We design the secure architecture that fits your obligations and your stack, mapped to published frameworks — NIST AI RMF, ISO 42001, OWASP LLM Top 10, MITRE ATLAS — not vendor marketing. Your private AI program is built on evidence and control.

Questions Worth Asking

Direct answers to what leaders ask before committing to private AI.

"Isn't a private LLM just worse than a frontier cloud model?"

For many enterprise use cases — retrieval over your own documents, classification, drafting, internal Q&A — a well-chosen open-weight model running privately is more than capable, and the data-control and cost advantages are decisive. We help you match the model to the use case so you are not paying a frontier premium for a job a private model does well.

"Do you build and host it for us?"

This is advisory and architecture. We design the secure deployment, recommend the models and controls, and produce a build-ready plan — then guide and review while your team or your integration partner implements it. You keep ownership of the build and the infrastructure.

"We're a Microsoft shop. Does private AI mean leaving Microsoft?"

No. Private and local AI complements the Microsoft work we do — and Microsoft has private options of its own. This advisory is vendor-neutral: we recommend what fits your obligations and your stack, whether that runs on your existing infrastructure, private cloud, or at the edge.

"How do we know a private model is actually secure?"

Running a model on your own hardware is not automatically safe — it can be a soft target on your own network. We harden the deployment and map controls to the OWASP LLM Top 10 and MITRE ATLAS, with an evaluation and acceptance plan so you can prove it meets your bar before it goes live.

Keep your data in-house.

Talk to Us About Private & Local AI.

Tell us why you need AI to run where you control it — sovereignty, regulation, air-gap, or cost. We'll design the secure architecture and a build-ready plan your team or partner can execute.

Book a Discovery Call

Keep your AI — and your data — in-house.