We built the tool
we wished we had.

After a decade of doing SEO, content, and digital PR for hundreds of clients, we stopped looking for the right tool and started building it.

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As an agency doing SEO, content, and digital PR for over a decade, we kept running into the same walls. Every new client meant rebuilding processes. Our best analysts' knowledge lived in their heads. We'd spend days on research that should take hours.

The tools existed but they didn't talk to each other, and none of them knew how we do things. We had keyword tools that couldn't write strategies. Content tools that didn't understand SEO. PR tools that couldn't find the right journalists. And none of them could chain together into the actual workflows our team runs every day.

Every time a senior analyst left, their expertise walked out the door with them. We were always starting over.

We tried every AI tool, every automation platform. ChatGPT, Jasper, copy.ai, Zapier, Make. We spent months evaluating, integrating, building custom workflows on top of tools that weren't designed for what we actually do.

They all had the same problem -- they were generic. They could write a blog post but not the way we write them. They could pull keyword data but didn't know what to do with it. They required as much setup as doing the work manually.

The AI was impressive. But it didn't know our work. It didn't know that a competitive gap analysis isn't just keyword overlap -- it's understanding intent shifts, content quality signals, and link opportunity mapping. It didn't know that a PR pitch list needs beat relevance scoring, not just contact info.

We didn't need another tool.

We needed to take everything we'd learned -- every process, every methodology, every hard-won insight from thousands of campaigns -- and turn it into AI that works the way we do.

Not a chatbot with access to Google. Not a template library with some automation bolted on. Something fundamentally different: AI that has actually been taught how to do the work.

The difference between AI that can help and AI that actually knows is the difference between a search engine and an expert.

Three layers. One platform.

We broke the problem into three parts, each solving a different piece of the puzzle.

01
AI Experts trained on real research
Not just prompts -- actual knowledge bases built from hundreds of sources. Our SEO expert was trained on real methodologies, real case studies, real competitive frameworks. It doesn't guess what "good" looks like. It knows, because we taught it.
02
Skills that codify exactly how our team works
Every repeatable process our analysts run -- competitive analysis, content audits, journalist research -- captured as a skill with specific steps, specific tools, and specific output formats. Same quality whether it's your best person or your newest hire.
03
Automations that run complete processes
Deterministic workflows that run end-to-end. Put data in, get deliverables out. No AI improvisation -- same input, same output, every time. Run it once or schedule it to run on every client, every month.

Generic AI gives generic answers.

Ask a generic AI tool to build a link building strategy. You'll get a listicle of obvious tactics pulled from blog posts anyone can find. It's technically correct. It's also useless.

Ask our AI the same question. It'll pull your current backlink profile, analyze your competitors' link gaps, identify content formats that earn links in your industry, and prioritize by effort-to-impact ratio -- because that's how our team actually does it.

Our AI gives answers informed by a decade of doing this work. That's the difference between a tool that can help and a tool that actually knows.

We didn't build a smarter chatbot. We built our team's expertise into software.

Ready to put our expertise to work?

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