Build a content system that compounds month over month. More pages. More citations. More buyers finding you in AI search — and choosing you.
Phase 1 made AI engines able to read your site. Now the problem is scale. Your competitors are publishing relentlessly. AI citations go to whoever has the most relevant, structured content. Right now, that's not you.
Every page they publish is another chance to be cited. They're building content systems. You're still producing pages one at a time.
Blog posts get written, published, and forgotten. There's no system, no structure, no strategy for what each piece is supposed to do in AI search.
Buyers are asking AI very specific questions your brand should own. You don't have pages for them. Someone else does.
Enterprise buyers use AI agents to research and compare solutions. Without MCP server integration, your product is invisible to those agents.
Phase 2 combines two content types that serve different roles in AI search. Understanding the difference is the key to understanding why this system works at scale.
Deep, research-led content on topics your buyers ask AI about. Each page takes real strategic thinking — a unique angle, structured arguments, expert positioning. These are the pages ChatGPT, Perplexity, and Google AI Overviews read and reference directly.
Best for: Earning citations, building authority, owning topic categories
Built from a template + a data source. The structure is fixed; the variables change. Think of it as a content pipeline — you build it once, then it produces hundreds of targeted pages automatically. Each one covers a specific combination of query, industry, location, or use case.
Best for: Capturing long-tail demand, covering query variations, scaling coverage fast
Phase 2 starts with a one-time setup — then a monthly retainer that keeps the engine producing. Here's exactly what each stage covers.
We map your full topic universe — every cluster of queries your buyers use when researching in AI. We structure this into a content architecture: which editorial pages to build first, which programmatic templates to create, how everything links together.
We identify the exact questions buyers ask ChatGPT, Perplexity, and Google AI when researching your category. Not generic keywords — specific AI prompts. We group them by intent (awareness, comparison, decision) and assign each to a content type.
We build the page templates and data pipelines that power your programmatic content. This is the infrastructure layer — once built, it runs automatically each month producing new pages from new data.
We build and deploy a Model Context Protocol server that connects your product directly to AI agents. Enterprise buyers using AI tools to research solutions can now find and interact with your product data in real time.
New research-led articles published every month. Each one targets a high-priority query cluster, is structured for AI citation, and feeds into your entity authority. Volume scoped at discovery based on your industry and content maturity.
The template system runs every month producing new pages from updated data. New industries, new use cases, new competitor comparisons, new locations — whatever your data source covers, new pages are published automatically.
Weekly review of citation data, content performance, and query coverage. We adjust, expand, and optimise based on what's working. What earns citations gets scaled. What doesn't gets improved or replaced.
Content doesn't disappear. Every page published in month 1 still works in month 6. The system doesn't reset — it stacks.
Example based on a mid-size SaaS client. Actual volumes scoped at discovery.
Full topic cluster map, query database, and editorial + programmatic content plan.
Templates + data pipeline built and deployed. Runs automatically each month.
Deep, research-led articles engineered for AI citation. Volume scoped at discovery.
Automated targeted pages covering query variations at scale. Volume scoped at discovery.
Your product integrated into AI agents. Enterprise buyers researching with AI tools can now find you.
AI agents running content, monitoring, and optimisation continuously — not just when someone is working.
What earns citations gets scaled. What doesn't gets fixed. Continuous improvement, not set-and-forget.
Where you're being cited, which platforms, which queries, and how it's trending month on month.
The setup fee covers the content architecture, template system build, first batch of pages, and MCP server deployment. This is where the majority of the strategic work happens — and it means month 1 of your retainer is producing results, not still planning.
| Site scale | What it means | Setup fee |
|---|---|---|
| Up to 100 pages | Focused site, clear topic territory | $2,500 |
| 101–500 pages | Established site, multiple topic clusters | $4,500 |
| 500+ pages | Large site, complex architecture needed | $7,500 |
Page volumes are scoped during the discovery call — not a flat number applied to everyone.
This is scoped during the discovery call — not a flat number applied to everyone. A new SaaS site needs different volumes than an established e-commerce brand. What matters is the right pages for your situation, not the highest number we can promise.
Editorial pages are deep, research-led articles built to earn AI citations — like "How AI search works for SaaS buyers in 2026." Programmatic pages are built from templates + data to capture long-tail demand at scale — like "Best CRM tools for healthcare" replicated across 50 industries. Both types are included in Phase 2.
Yes, in almost all cases. Phase 2 builds a content engine on top of a technically optimised, AI-readable site. Without the Phase 1 foundation — entity data, schema, content structure — the content engine produces pages that AI can't properly read or cite.
A Model Context Protocol server connects your product data directly into AI agents. Enterprise buyers increasingly use AI tools to research, compare, and shortlist vendors — an MCP server means your product is natively accessible in those tools. If your buyers are enterprise or mid-market, you need one.
Editorial pages typically generate first citations within 6–10 weeks. Programmatic pages begin driving long-tail traffic within 4–8 weeks of indexing. The compound effect — where your growing page library drives exponentially more citations — typically becomes visible at months 3–4.
You can continue on a monthly basis, scale up to Phase 3 (AI Marketing OS), or — if you want to bring the system in-house — we document everything and help you transition. Every page, every template, every workflow we build stays with you.
Start with a free AI Visibility Audit. We'll show you your current citation gaps, your competitors' content advantage, and exactly what Phase 2 would address. No obligation.
AI is already choosing who gets recommended — and who gets ignored.
Visibility is no longer about ranking. It's about being selected.
Our proprietary framework — The Lifecycle of AI Discovery