Turn AI ambition into governed, working change.

IntentOS is the governed context layer that makes marketing AI safe and useful — grounding Claude, ChatGPT and Copilot in your live market, with humans in the loop.

Challenges

What it's costing you

Executive AI ambition with no operating model to deliver it
Shadow AI in pockets, no standards, inconsistent quality
GDPR, IP and brand-voice risk with third-party LLMs
Stalled pilots; unclear KPIs and no agreed sign-off

The result? AI spend without measurable value — and brand and compliance risk no one is governing.

How IntentOS helps

A governed shared context delivered into LLMs via MCP — grounded and on-brand
Human-in-the-loop QA, an audit trail and clear data boundaries
A real operating model — roles, workflows and approvals, not just tools
Brand-voice guardrails so scale doesn't mean drift
Measurable KPIs tied to decisions, plus enablement to build in-house capability

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Proof & outcomes (30/60/90)

30 days:

agreed governance boundaries, approved use cases and a sign-off model

60 days:

first governed AI workflows live with QA, audit trail and brand-voice controls

90 days:

measurable productivity gains with auditability and a repeatable pipeline

Governed by design

B Corp, Cyber Essentials, human-in-the-loop

Vendor-neutral

no lock-in; you own the assets

Enablement that sticks

role-based, not dependency

Safe, secure AI — that actually ships.

The gap in most marketing AI programmes is not ambition, it is governance and an operating model. Speed without strategy just scales mediocrity, and ungoverned tools create real brand and compliance risk. IntentOS closes the gap by making your live market and brand context the grounded source LLMs work from — delivered through MCP into Claude, ChatGPT and Copilot — with human-in-the-loop QA, an audit trail and clear data boundaries built in. On top sits a practical operating model: approved use cases, sign-off flows, brand-voice guardrails and role-based enablement. You move from scattered experiments to a repeatable, measurable pipeline leadership, legal and the brand can trust.

'AI introduces compliance and brand risk — we can't move fast.'

We propose a controlled operating model: data boundaries, approved use cases, human-in-the-loop QA and an audit trail. Done this way, speed reduces risk and rework rather than adding to it.