Put AI to work. Securely.
We help you control how AI uses your data and build applications your team can use safely. Start with the tools already in use.
Security for the AI you use and build.
Browser chat, built-in assistants, and custom apps each need different controls.
Govern ChatGPT, Claude, Gemini, Copilot in the browser. Redact sensitive data before upload instead of blocking the tool.
The same policy on Salesforce, your SIEM, and the agents on every machine.
Sales, legal, and HR are already shipping. Discover it, then put rails around it.
Apps with an LLM at the core. Secure the data, the RAG, and the app, and log all of it.
Set the policy. Help people use it.
Blocking it only moves it.
A ban pushes AI onto personal machines. Governance starts with seeing what is already in use.
- Which tools are in use, sanctioned or not
- Policy in the browser, where the prompt is typed
- Sensitive data redacted before it is uploaded
- Every prompt logged, attributed, reviewable
People still need a way to build.
Telling people AI is allowed does not teach them to build. Without a pipeline, the work worth having gets built outside everything you just governed.
- Standards written down, so nobody has to guess
- A review that takes days because it is automated
- Guardrails enforced in the pipeline
- Someone to operate it when your team cannot
A defined project, not an open-ended consulting spend.
Four phases with real timelines. It starts with an assessment and can extend into ongoing managed support.
Find the targets
- Identify application targets
- Deployment environment
- Management and operation needs
Stand up the stack
- Configure infrastructure
- Application standards
- Security stack
- Local and public LLMs
Build the applications
- Develop replacement applications
- Build additional features
Keep it running
- Patch and update
- Operational monitoring
- Security monitoring
It begins with an assessment and a working framework you keep.
Find the use cases
We talk with your people about their work and identify tasks AI could help with. You get a ranked list of what to build and what to automate.
- Every use case we found, scored on value and effort
- A build, automate, or leave-alone call on each one
- The security gaps standing in the way, and what the first build costs
Stand up the guardrails
We publish your AI development standards and build the infrastructure behind them, so your people ship apps faster and more safely. Those apps run in your environment, with guardrails we design.
- Written AI development standards and an acceptable-use policy
- A model router that decides who reaches which model, with spend capped and every call logged
- A build pipeline with SAST, DAST, and dependency checks wired in
- Your AI portal, live
130 people across HR, legal, and sales, each building apps that need updates. None of them can wait in a two-month review queue. We operate it when you won’t.
What buyers ask first.
Where do we start?
With the assessment: three to five days, fixed scope, fixed price. It surfaces your use cases and your gaps and leaves you with a framework you keep.
Can you govern AI our people are already using?
Yes. Browser-level policy that redacts sensitive data before upload, discovery of AI in use whether sanctioned or not, and the logging and audit you need.
Do sensitive prompts have to leave our environment?
No. The model router can send prompts to internally hosted models, so sensitive data never leaves. Public frontier models are available for everything else, with cost caps and per-group control.
Do you use AI in your own SOC?
Yes. AI runs the first mile: ingest, correlate, enrich, score, draft the summary. A named engineer owns the decision. We recommend the same order to customers: people and process first, then data, then the machine.
Find out where your organization stands, in about a week.
The assessment identifies use cases, security gaps, and the infrastructure you need to get started.