INSIDEMAVS SECURE CHAT
One governed workspace for every frontier model, with guardrails, role-based access and an audit trail for the data privacy regulations you answer to.
Secure AI WorkspaceFor Knowledge Workers
PII and business data protected at runtime
Personal data and business-sensitive values are replaced with synthetic stand-ins at runtime, so the models get full context and real data stays secure.
Configurable workspaces with guardrails
Admin sets policy per team, so each workspace enforces the rules that fit the type of work.
LLM agnostic, no vendor lock-in
OpenAI, Claude, Gemini, and others behind one interface. Change the model without changing your security posture or asking anyone to move tools.
It Reads Your Attachments Too
Your people can attach files as normal. Sensitive values inside them are replaced at runtime, exactly as they are in a typed prompt.
Risk and productivity dashboards
Top risks, prompts processed and adoption by team on a single screen. You see where exposure sits and where AI is genuinely being used.
Auditability and reporting
Every prompt and every replacement is logged. Export the record when an auditor asks, instead of reconstructing it after the fact.
Frequently Asked Questions
What is Mavs Secure Chat?
A governed AI workspace your team uses instead of going to public AI tools. They get every frontier model from one interface. You get policy set per team, sensitive data protected before it reaches the model, and an audit trail of every prompt.
Do we need to install anything?
No. Mavs Secure Chat runs in the browser, with no deployment and no IT overhead. Sign-in goes through your existing identity provider, such as Microsoft Entra ID.
Can we give different teams different models?
Yes. Model access is assigned per team. Because Mavs routes over API rather than being tied to any one vendor's app, you can put any model behind any team and change it later without moving anyone to a different tool. Finance and engineering do not have to share a model, or a policy.
What policies can I configure?
Policy is a ladder, set per team and applied per prompt:
- Reprompt with synthetic data. Sensitive values are replaced with realistic stand-ins and the prompt goes through. This is the default.
- Reroute internally. The prompt is sent to a model inside your own environment instead of an external one.
- Human in the loop. The person sees what was flagged in their prompt and decides whether to send it anyway. For employees you trust to make that call.
- Monitor only. Nothing is changed, everything is logged.
- Block. Available, and deliberately the last resort.
Why does Mavs reprompt instead of blocking?
Because blocking costs you the answer, and redaction quietly costs you accuracy. A placeholder like [NAME] or [AMOUNT] gives the model nothing to reason over, so it fills the gap by guessing, and guessing is where hallucinated output comes from. Synthetic stand-ins behave like the real values: same shape, same type, same relationships between them. The model reasons over them correctly and the answer comes back right, while the real data never leaves. Protecting data this way improves the output rather than degrading it, which is why reprompting is the default and blocking is the last resort.
How is this different from our DLP?
DLP works on rules, so it watches for known patterns and for files leaving the business. A prompt does neither, which is why it slips past: nothing is uploaded, someone just types a question that happens to contain the thing you care about. And much of what matters was never going to match a rule anyway, because a project codename or next quarter's pricing is only sensitive once you know what it refers to. Mavs reads the prompt itself and judges what is sensitive in that context, rather than checking it against a list.
Do my people have to learn a new tool?
No. It is a chat window. They type, they get an answer. The security happens between the prompt and the model at sub-second latency, invisible to them.
Can we meet data residency requirements?
Yes. The real values never reach the model, so where the model provider processes is beside the point. What matters is where Mavs runs: our cloud in a region you choose, or entirely inside your own environment. That is where your data stays.
