Operations practice

AI scales content. It scales mistakes at the same speed.

We evaluate AI-assisted marketing for factual accuracy, claim consistency and regulatory exposure before it is published — and put the review process in place that keeps it that way at volume.

Request an evaluation See the flagship sprint

The problem

Fluent and wrong is the worst combination in marketing.

Generative tools produce persuasive copy, including claims that are subtly inaccurate, quietly off-brand or non-compliant. At human volume those slip through occasionally. At AI volume they become systematic.

There is a second cost that is easy to miss. Inaccurate claims published at scale become part of the public record about your company. An error repeated across forty pages can look, to a reader or an AI system, like a well-supported fact.

That makes accuracy a positioning concern as well as a compliance one. A review layer is what lets a company use AI-assisted content without undermining the evidence its reputation depends on.

Regulated and reputation-sensitive firms
AI-assisted content at volume
No review function today
Claims that are also compliance statements

Deliverables

What you receive.

Fabrication assessment: Invented facts, false specifics and unsupported attributions found across a representative sample.
Claim and brand consistency: Whether claims and positioning hold across outputs, or whether three versions of your company have appeared.
Factual accuracy testing: Statements checked against your source of truth, with disagreements listed rather than summarised.
Compliance review: Language that creates regulatory or legal exposure, flagged before publication.
Public-record check: Published inaccuracies already circulating, and what it would take to correct them.
Review process: Approval gates and guardrails that let you keep publishing without repeating this exercise every quarter.

This sits in the operations practice. It uses the same claim-to-evidence discipline as the positioning work, applied to content at volume. About the sprint

Why it matters now

Trust is slow to build and fast to lose. AI changed only the second half.

The companies that do well with AI-assisted marketing are not the ones producing the most. They are the ones producing at volume without compromising accuracy, brand or compliance.

Put review around the output and speed becomes an advantage. Leave it off and you are scaling the production of claims you will later have to retract.

Questions

On marketing evaluation and reliability.

The opposite. Review is what lets you use them harder.

Both models exist: a single evaluation and a review process you run yourselves, or ongoing evaluation as a managed layer. We scope it to your volume and risk.

A copy edit improves prose. This assesses factual accuracy, claim consistency, compliance exposure and reputational risk before publication.

Questions buyers ask before they ask a firm.

How do AI systems decide which companies to recommend? What is documented, what is inference, and what nobody outside the providers knows. How do you get recommended by ChatGPT? The honest answer, in order of how much each move is likely to matter.

Check your AI-assisted marketing.

Before you scale it, request an evaluation. We will show you the accuracy, brand and compliance risks in what you already publish.

Request an evaluation All engagements

If we can help, you leave with one specific observation about your positioning. If we cannot, we say so.

Other engagements

Where this fits.

Positioning & Evidence SprintFour weeks on one product, one use case and one buying group: a defensible position, with the evidence behind it put to work. Market & Opportunity MappingBefore the sprint: decide which market, use case and buyer to pursue first. AI Search Visibility AuditA repeatable sample of what AI assistants say about you, checked against your approved facts. Positioning & Category StrategyFor a broader repositioning: several products or audiences, or a decision about which category to compete in.