Secure enterprise AI

Secure generative AI deployment

Private LLM systems for environments where a leak is unacceptable

Generative AI is straightforward to demonstrate and hard to deploy responsibly. We build LLM systems that run inside your boundary, answer from your own material with the source attached, and come with a way to measure whether the answers are actually good.

Three failure modes, and what to do about them

The data leaves. The default architecture sends your prompts (often containing the sensitive material itself) to a third-party endpoint. Where that is unacceptable, the answer is self-hosted inference inside your network, with an offline installation path for sites that have no outbound connectivity at all.

The model invents. A fluent answer drawn from the model's general memory is worse than no answer, because it is convincing. Retrieval-grounded generation with every passage linked back to its source document lets a human verify rather than trust.

Nobody measures it. Most deployments have no idea whether retrieval quality is improving or degrading. An evaluation harness scored against your own benchmark questions and expert answers turns that into a number you can track before and after every change.

What a secure GenAI engagement covers

Self-hosted inference

Models run inside your infrastructure: on-prem, air-gapped, or your own cloud enclave. Query content stays on your network.

Source-linked retrieval

Hybrid search combining meaning-based retrieval and re-ranking with exact keyword lookup, so both conceptual questions and precise term searches work.

Evaluation harness

Retrieval accuracy scored against your own benchmark questions and expert answers. Measure before you trust, and again after every change.

Access control

Role-based access and approvals, so the system respects the document permissions your organization already has.

LLMOps

Fine-tuning, retrieval tuning, evaluation, and the operational patterns needed to run language models at enterprise scale. See Deployment & MLOps.

ISO 42001-aligned governance

Risk, bias, and audit controls built into delivery rather than retrofitted when a certification deadline arrives.

The fastest path is often a product

If the requirement is "our engineers need to ask questions of our own documentation, and the documents cannot leave the network", that is a solved problem: SnowShoe.ai is our platform for exactly that, and it ships with an offline installer, an acronym dictionary, role-based access, and the evaluation harness built in.

Where the requirement is more specific, we build it, and you own the result. Platform licensing and consulting IP ownership are kept deliberately separate; see products for the licensing language.

Registered to contract in the United States

U.S. and NATO registration is already in place, so teaming does not wait on paperwork. Every identifier is verifiable in a public registry.

Unique Entity IDJJADR6LWJ743
NCAGE codeL0QB4
U.S. consortiumSOSSEC member
CybersecurityCMMC, scope on request
Controlled goodsCGP registered
PersonnelTop Secret cleared staff

Registry entries establish procurement eligibility. They do not imply endorsement by any government. Full detail is in our capability statement.

Scope a secure deployment

Tell us what the material is, where it has to stay, and who needs to query it.

By submitting this form, you agree to be contacted about your enquiry. See our Privacy Policy.

What happens next

  1. An engineer reads it and replies to confirm fit.
  2. A 30-minute discovery call to frame the problem, constraints, and success measures.
  3. A fixed-fee scope with deliverables, milestones, and price, typically within two weeks.

You own the code, models, and documentation we produce. Defense and government enquiries can go direct to [email protected].