Local LLM solutions

Run AI where your data lives

For regulated workloads, sending data to a cloud model is a non-starter. We select, design, build, and secure self-hosted LLMs that run inside your own boundary: same AI capability, zero data leaving your infrastructure, and the audit evidence to prove it.

Not a platform vendor trying to sell you hosting. The practitioners who design your stack are the same hands who run, tune, and break the inference infrastructure in your boundary.

And no pressure either way. We're happy to just talk through whether local even makes sense for you. If it doesn't, you don't need us, and we'll tell you.

Free architecture call

What we deliver

Select, design, build, secure: as one engagement

We take local AI from "where do we even start" to a hardened, running deployment, with security and compliance woven in rather than bolted on after. One team, end to end, accountable for the whole thing.

Pillar 01 / 04

Select

Choosing a model is a trade across capability, footprint, licensing, and compliance. We benchmark open and commercial models against your actual workloads and pick the one that fits, not the one with the best marketing.

Pillar 02 / 04

Design

We architect the deployment: hardware sizing (CPU/GPU/RAM), inference serving (vLLM, TGI, llama.cpp), retrieval and RAG wiring, and API design. Built to run reliably inside your own boundary.

Pillar 03 / 04

Build

We stand up the stack, containerized, observability-instrumented, and reproducible, so you can deploy, scale, and roll back without guesswork. Your data stays on your infrastructure.

Pillar 04 / 04

Secure

Local doesn't mean automatically safe. We harden the model host: access controls, prompt-injection and jailbreak defense, data exfiltration monitoring, model tamper protection, and audit logging to satisfy the auditor.

Why go local

Data sovereignty isn't a preference. It's the requirement

Cloud AI is right for many things. For regulated data, self-hosting is the only answer that keeps you defensible. Here's what you protect, and the work we do to protect it.

Data never leaves your boundary

Patient records, financial data, and IP stay on your infrastructure, with no third-party model provider, no training on your data, and no cross-border transfer.

Compliance and auditability

Bring models inside HIPAA, CMMC, FFIEC, and SOC 2 boundaries with full logging and evidence your compliance team can actually defend.

Deterministic cost and control

Predictable infrastructure cost instead of per-token drift, plus full control over versions, fine-tunes, and availability.

Offline and sovereign operations

Keep AI working in air-gapped, classified, or network-constrained environments where cloud models simply can't go.

What we build

The full stack, end to end

From model choice to the guardrails on top. We deliver a complete, reproducible local LLM deployment, and we're the ones who run and maintain it.

Model & framework selection

Open vs. commercial candidates benchmarked on quality, latency, license, and footprint. We recommend; you decide.

Hardware & capacity planning

Realistic GPU/CPU/memory sizing for your concurrency and quality targets, with no overspend and no surprise refits.

Inference serving & scaling

vLLM, TGI, llama.cpp, or a custom path, tuned for throughput, latency, and graceful degradation under load.

RAG & data plumbing

Embeddings, retrieval, and vector storage wired so answers are grounded in your data, and provenance is recorded.

Guardrails & safety layer

Prompt-injection and jailbreak resistance, output filtering, and tool-access controls layered onto the host.

Security & compliance hardening

Access control, network segmentation, logging, monitoring, and audit evidence mapped to the frameworks you run under.

The process

From workload to hardened deployment in five steps

01

Scoping & workload profiling
Step 01

We identify the workloads that justify local, the quality bar they need, and the non-negotiables (data residency, latency, budget).
02

Model & hardware selection
Step 02

We benchmark candidate models and size the infrastructure, making the build-buy-local decision on data, not vendor claims.
03

Architecture & build
Step 03

We design and stand up the stack, including serving, retrieval, security controls, and observability, containerized and reproducible.
04

Hardening & testing
Step 04

We pen-test the deployment: prompt injection, jailbreak, exfiltration paths, and access control, and fix what we find.
05

Operate & iterate
Step 05

We hand over runbooks, monitoring, and retraining cadence, and can stay on to operate or fine-tune as models evolve.

Moving AI into your data center moves the risk off your vendor's terms, and onto yours. Make sure you control the whole stack.

Principle Security Advisory Team

Questions

What engineering leads ask first

When does running an LLM locally actually make sense?

When data residency, compliance, offline operation, or cost predictability matter more than bleeding-edge model quality. For many regulated workloads, including PHI, financial data, and IP, local is the only defensible choice.

Isn't local AI a huge cost and engineering lift?

It can be, if done badly. We size infrastructure honestly and match workload to model tier, so you only run local where it earns its keep. Many clients run a hybrid: local for sensitive data, cloud for the long tail.

Will a local model match GPT-class quality?

For many tasks, yes. Open models have closed much of the gap, especially with good retrieval. Where they don't, we tell you plainly and recommend hybrid before you over-invest.

How is this different from your AI Agent Security work?

Agent security hardens agents that act. This offering secures the model hosts themselves, including the inference stack, data plumbing, and perimeter. A local deployment needs both.

Do we need GPUs on-prem for this?

Often yes for good quality, but not always. We'll tell you the honest hardware envelope for your workloads, whether on-prem, a hosted private cloud, or a mix, based on your data constraints.

Do you run it for us after build?

If you want. We can operate, monitor, and fine-tune the deployment on an ongoing basis, or hand you runbooks and step back. Your call.

Ready to start

Run AI inside your boundary, securely

Book a free architecture call. We'll map the right workloads, size the infrastructure honestly, and show you the secure path to a self-hosted model, then build it for you.

No pitch deck, no obligation. If local isn't the right call for you, that's the conversation to have. We'll be straight about it.

100%

Data stays in your boundary

Pillars: select · design · build · secure

Training on your data

24/7

Optional managed operations

Request a local AI scoping call →

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