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Secure AI

Useful AI without treating private data as public material.

We design the boundary before choosing the model. Hosted, private-cloud, and self-hosted systems can all be appropriate when identity, retrieval, tools, logs, and retention are engineered together.

Business outcomes

What the work can change.

  • 01Give teams secure access to coding and reasoning agents
  • 02Use sensitive documents without uncontrolled retention
  • 03Host a model inside your security boundary
  • 04Add approval, evaluation, and audit to an existing AI workflow
Delivery

How engineering moves the work.

  1. 1Classify the data and threat model
  2. 2Choose the appropriate inference and hosting boundary
  3. 3Constrain identity, retrieval, tools, logs, and retention
  4. 4Test leakage, accuracy, abuse, and failure recovery
Representative work

Proof in production context.

Start with the real problem

Talk directly with an engineer who can help build it.

Bring the current system, the desired outcome, and whatever uncertainty remains. We will help identify the smallest credible path to production.

Talk to an engineer →