An AI Marketing Agency Built on a Framework, Not a Buzzword

FAQs

Frequently Asked Questions

What's the difference between AI marketing and traditional digital marketing?

Traditional digital marketing uses people and rules to plan, target, and measure campaigns. AI marketing replaces parts of that workflow — targeting, bidding, creative variation, content drafting, and attribution — with models that learn from data. The strategy still has to come from humans. The measurement still has to be honest. AI just compresses the time between insight and execution.

How is GEO different from SEO?

SEO optimises content so it ranks in Google's blue links. GEO (Generative Engine Optimization) optimises content so it gets cited inside AI answers from ChatGPT, Perplexity, Gemini, and Claude. The technical work is different — entity clarity, structured citations, source authority — and the success metric is different. SEO measures rank and clicks. GEO measures inclusion and attribution inside AI answers.

Can AI replace our marketing team?

No. AI replaces specific tasks inside marketing workflows — drafting, targeting, bidding, scoring, summarisation. It does not replace strategy, brand judgment, customer empathy, or accountability for results. Teams that use AI well tend to get smaller in production roles and larger in strategy and analysis roles.

What's a realistic ROI on AI marketing investment?

It depends entirely on which rung of the AI Enablement Ladder™ you're investing in. Stage 1 and 2 typically produce 10–25% productivity gains in specific workflows. Stage 3 (integration) is where revenue impact starts — typically 15–40% lift on the integrated channel. Stage 4 produces compounding gains because the system improves continuously. We model this up front so you're never investing without a defensible ROI projection.

Do we need our own data to use AI in marketing?

For Stage 1 and 2 work, no — public foundation models are enough. For Stage 3 and 4 work, yes — your own customer data, conversion data, and content corpus are what create defensible advantage. A core part of the Readiness Audit is a data inventory: what you have, what you're missing, and what you'd need to collect to unlock the next rung.

How do you measure AI marketing performance?

Same way we measure any marketing: against business outcomes — pipeline, revenue, contribution margin, customer lifetime value. AI is just a means. We use the Signal vs Noise™ framework to keep AI dashboards focused on decision-grade metrics instead of impressive-looking activity metrics.

What AI tools do you use?

We're tool-agnostic. The tools should fit the stage. Stage 1 and 2 typically use OpenAI, Anthropic, Google Gemini, and Perplexity. Stage 3 adds Snowflake, BigQuery, Hightouch, vector databases, and custom model fine-tuning. We pick the stack after the audit, not before.

How do you keep our customer data safe?

Customer data stays in your environment. We work inside your cloud (AWS, GCP, Azure, Snowflake) where possible. When we use third-party models, we use enterprise tiers with data-processing agreements that prohibit training on your data. All access is logged, scoped, and time-limited.

How long until we see results?

Productivity gains from Stage 1 and 2 work are usually visible within 30–60 days. Revenue impact from Stage 3 integration work usually shows up within 90–180 days because models need data and time to learn. We set milestone-level expectations in writing during the audit.