Encrypted-in-use AI for missions that cannot fail.

US natsec, defense, IC, and systemic risk

• US national security & defense: IC agencies, DoD components, DARPA/IARPA-style programs that need encrypted-in-use models, twin deployments, and audit-grade interpretability.
• Defense & intel industrial base: primes and niche defense AI vendors that must embed robust, explainable, and cryptographically hardened AI in real systems.
• Systemic finance & markets: exchanges, SIFIs, and elite risk/quant shops where encrypted stress tests and explainable models are regulatory, not optional.
• Critical infrastructure / OT: grid, pipelines, rail, aerospace manufacturing, and industrial control where failure modes are physical and irreversible.

Strategic alignment These actors have the strongest overlap with GRAIL/Λ-Stack/MIA: mission risk, appetite for deep math, and budgets for non-commodity IP.

Healthcare, regulated enterprise, and platforms

• High-security healthcare / clinical AI: multi-center studies and PHI-heavy workflows that need encrypted models and calibrated risk scores.
• Regulated enterprise: energy, pharma, aerospace, and advanced manufacturing that care about IP protection, audit, and sovereign AI.
• Cloud & hardware vendors: future platform licensing of encrypted-in-use engines, invariant-first NPUs, and twin-model infrastructure.
• Academic / non-profit labs: collaboration partners for physics, biology, and λ-Stack research—more for credibility and science than for revenue.

Role Expansion segments once the core stack is proven; they benefit from the same λ-secure foundation without driving the initial roadmap.

Law, founders, and high-confidentiality civil orgs

• Law firms & in-house legal: cannot upload client files to random LLM APIs; need on-prem or tenant-isolated private GPTs with a stronger story than “trust our logs.”
• Privacy-first founders & indie teams: treat their data as the moat; want local or BYO-cloud LLMs without leaking core IP into big vendor models.
• High-confidentiality civil / religious / humanitarian organizations: religious orders, diocesan structures, professional bodies, and select NGOs that require sovereign control over internal documents and archives.

Why they matter These segments are ideal early sandboxes: shorter cycles, unclassified workloads, and strong privacy instincts that stress-test the λ-secure stack before it enters classified or systemic domains.

λ-secure private GPT (sub-1B)

• Base model in a pseudo-Riemannian latent space, designed to run on a single RTX-class GPU or modest server.
• Custom fine-tuning inside your perimeter, with your own secret transform T (Lorentz/orthogonal in a Minkowski-style frame).
• Target use-cases: contracts and policy, internal knowledge search, red/blue analysis notes, and mission or matter drafting.

Deployment Self-hosted on your on-prem GPU, in a SCIF, or in a tenant-isolated cloud instance you control. Designed to be upgradeable to classified or export-controlled regimes.

MIA VM — λ-secure AI OS

• A virtual machine / container image with: λ-geometry runtime, an auto-training agent, and connectors to open-source models (7B / 8B / 70B via your chosen tooling).
• You pull model weights from official sources using your own credentials; Logarchéon does not re-host foundation models.
• One semantics, many environments: laptop, rack server, on-prem cluster, or your own cloud tenancy.

Upgrade path Tier III sandboxes start with a sub-1B λ-secure model; Tier I/II deployments grow into full MIA VMs with multiple models, agents, and red/blue workflows.

Hardware appliance (roadmap)

• A small, hardened “λ-secure AI box” built on FPGAs / mature-node silicon for OT, forward-deployed, or air-gapped use.
• Drop into a rack, connect power + internal network; keep everything air-gapped from the public internet if required.
• Same λ-geometry and T-transform semantics as the software stack, plus a physical security posture for high-assurance missions.

Status Design underway. Early partners in national security, defense, critical infrastructure, and systemic finance can help shape specs and certification targets.

“We can’t just upload client files to random LLMs.”

• Client confidentiality, privilege, and bar ethics make public LLM APIs a non-starter.
• You need tools that live on your own hardware or in your own cloud tenancy, with a clear audit story.
• You want more than “we signed a DPA and trust the vendor” when explaining risk to partners and clients.

Why this segment first Law firms have sharp, well-defined pain around generic LLMs, can adopt a single-GPU λ-secure assistant quickly, and create clean case studies that translate upward into regulated enterprise, finance, and government use-cases.

“Our data is the moat; we refuse to send it to Big Tech models.”

• You want local or self-hosted LLMs that run on a single GPU or small server, not a sprawling cluster.
• You are willing to rent cloud GPUs—if the account, keys, and λ-secure transform T stay under your control.
• You need an engine that keeps your IP and tuned weights from becoming someone else’s training signal.

Why this segment first Privacy-first founders move fast, live close to the tooling, and are comfortable with open-source components. They are ideal partners to harden the MIA VM, λ-secure training, and single-GPU deployment path before those same primitives are presented to defense, IC, and critical-infrastructure programs.

You keep the secret T

• You run a setup routine (or the MIA VM) that embeds your documents or signals into the λ-geometry space.
• You choose a secret transform T on your side.
• The system applies T to both the model and your embedded data, producing T·M and T·X.

Training happens in the T-frame

• Training runs either on:
  • your own GPU/on-prem server, or
  • GPUs you rent in your own cloud account, using the MIA VM.
• Only T·M and T·X ever touch that compute environment.
• Neither Logarchéon nor the cloud provider sees plaintext data or a canonical tuned model.

Inference stays vendor-blind

• You can keep everything in the T-frame for operations, or locally apply T^{-1} if you need canonical outputs.
• Even if someone copies the VM or model, they obtain only T·M', not a usable canonical twin.
• Standard crypto (disk encryption, TLS, TEEs) sits underneath the λ-geometry layer as defense-in-depth.

• Cloud LLM SaaS: Data is “protected by policy,” but runs in plaintext inside someone else’s stack.
• On-prem legal/enterprise AI: Runs inside your network, but the vendor still sees a canonical model and often plaintext data when supporting you.
• DIY local LLMs: Full control, but you own all the complexity and there is no formal obfuscation or twin-deployment story.

• Single-GPU friendly: Sub-1B λ-secure models and 7B-class open models that run on a single RTX-class GPU or modest server.
• Zero-knowledge vendor stance: Logarchéon designs the geometry and training stack; you keep T, own the accounts, and control the runtime.
• By-design BYO-cloud: The stack is meant to run in your AWS/Azure/GCP tenancy (or on-prem). Logarchéon does not ask for production access.

• CEAS: critical-entropy attention scaling; treats β as a controlled parameter to cut training steps and improve stability, especially in long-sequence regimes.
• Finite-machine lift: decomposes model behavior into cycles and transients (Dunford D+N / PDN) for traceability and safe edit-without-full-retrain.
• GRAIL: geometry-native secure execution aimed at cryptomorphic (function-identical, weight-distinct) twins and encrypted-in-use computation.
• MIA: metric-invariant architecture where inputs, machine state, and outputs move together under group action; suitable for FPGAs and mature-node silicon.

If you are a technical reviewer, cryptographer, or ML researcher and want the math, proofs, and prototypes:

• Visit the Research page for notes, slides, and code snippets.
• See my CV for academic background and prior work.
• Or email for non-enabling technical briefs and NDA-gated materials where appropriate.

Expectation Public write-ups are intentionally non-enabling. Detailed materials are shared under NDA and, where relevant, export-control and classification review.