Why we built Targe for AI governance

Every executive team we speak to has the same two slides in their AI deck. The first is a long list of pilots and experiments. The second is a short list of policies, usually inherited from an unrelated risk framework. The gap between those two slides is where the trouble lives, and it is where we built Targe.

The race no one is winning

Companies across Namibia and South Africa — and across our wider client base — have moved from "should we try AI" to "we are already trying twelve things" in a single year. Marketing has a copywriter, ops has a forecast assistant, finance is piloting a reconciliation bot, and a junior analyst is quietly running confidential reports through a public chatbot from a personal account. Every one of those initiatives feels small. Together they are a governance problem.

The honest answer to "who approved that?" is almost never "no one" — it is "the wrong person, with the wrong information, on a Slack thread that no one can find."

Three things we kept seeing

Sensitive data leaks through the cracks. Not from a breach, but from copy-paste. A team member pastes a customer list into a model that retains it. A draft contract goes into a public assistant. There is no malice, only convenience, and no system that catches it in flight.

Approved tools quietly become unapproved. An assistant gets a new feature — a web browser, a code executor, a memory — and the original approval no longer reflects what the tool actually does. Nobody is watching the changelog.

The audit trail is a hunt. When the question comes from a board, regulator or customer — "show us how this decision was made" — the answer is rebuilt from screenshots, Slack messages and someone's memory. It is exhausting, and it is not evidence.

What we wanted Targe to do

We started from a simple test. If a leadership team asked, on a random Tuesday, "which AI initiatives are running, what data do they touch, who approved them, and against which policy" — we wanted the answer to take seconds, not weeks.

That shaped four product principles:

Initiatives, not tickets. Targe treats every AI use case as a first-class object — registered, reviewed, owned. It is not a Jira board with extra labels.

Policy as code, written in English. The rules a company already has — "no client PII in third-party models", "finance data stays in-region" — are expressed as policies inside Targe and evaluated automatically when an initiative is registered or changes.

Approve once, monitor always. An approval is a snapshot of what was true on a given day. Targe keeps watching: when a tool's capabilities change, or a data source moves, the affected initiatives are flagged for re-review.

Audit-ready by default. Every decision — who approved what, against which policy version, with what evidence — is written down at the moment it happens. There is nothing to reconstruct later.

What Targe is not

Targe is not a model. It does not generate text or replace your tools. It is not a procurement checklist that lives in a spreadsheet. It is not a gate that says no to everything — the goal is to make yes safer and faster.

Most AI failures inside a company are not technical. They are governance failures wearing a technical costume. Targe is the system of record that turns "we should govern this" into "we already are."

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