Platform

AI Governance

Every AI call in your enterprise. Governed.

An AI governance platform that sits between your people and the models they use. Every LLM request is intercepted, checked against policy, screened by local ML-based data-loss prevention, and logged — so AI adoption and AI governance stop being opposing forces.

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// the_problem

Your teams are already using AI. You just can't see it.

✕ Employees paste customer data into public chatbots
✕ No record of what was sent to which model, by whom
✕ Every team picks its own AI provider — nobody sanctioned any of them
✕ Cloud DLP tools mean sending sensitive data out to be scanned
✕ Blocking AI outright just drives usage underground
✕ The audit question "how is AI used here?" has no answer
// what_it_does

Enable AI. Keep control.

Forward Proxy for All LLM Traffic

Every AI call in your enterprise flows through one governed gateway — no shadow AI, no unsanctioned providers.

One egress route change puts every team, tool and model behind policy.

Local ML Data-Loss Prevention

PII, toxicity and sensitive-content detection runs on local ML models inside your network — sensitive data is caught before it ever reaches an external model.

Detection happens on your infrastructure. Nothing is sent out to be scanned.

Policy Enforcement

Define which teams can use which models for which purposes — and the gateway enforces it on every request, automatically.

Sanctioned provider lists, per-team rules, and block/redact/allow actions.

Complete Audit Log

Every prompt, every response, every policy decision — logged with time-series analytics behind a live dashboard.

When the regulator asks how AI is used in your business, the answer is a query, not a project.

// under_the_hood
Go
High-throughput gateway
Local
ML DLP — in your network
500+
Stored test cases
Live
Time-series analytics dashboard
// getting_started

Three things, and you're governed

01

Route egress

Point your network’s AI traffic at the proxy — one infrastructure change.

02

Define policy

Which teams, which models, which data classes. We help you write the first set.

03

Sanction providers

List the LLM providers you allow — everything else is blocked by default.

// how_we_deliver

See the work behind the platforms

Four client systems built by the same team — IT support, field-service scheduling, insurance sales and contractor compliance. Real numbers wherever clients have released them.

Read the case studies How a 4-6 week sprint works

Adopt AI without losing the audit trail

If you're in a regulated industry and your board is asking both "why aren't we using AI?" and "how do we control it?" — this is the answer to both.

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