How do you make sure AI recommends you and not your competitors?
Business Intelligence reads how AI recommends across your category and the exact language behind every call, then the Content Lab writes the pages and posts that close the gaps.
It starts by asking the same buying questions your customers ask, across four major AI models at once. What comes back is a clear read on who gets recommended and why, and the content plan to move that answer in your favor.
How does it work?
Three moves: measure how AI recommends, analyze the language behind it, then write the content that closes the gaps.
We run your category’s real buying questions across four AI vendors, study the recommendations and the reasons behind them, and hand the findings to the Content Lab to turn into pages and posts. The measurement comes first and the writing follows it, so the content is always aimed at a gap the read actually exposed.
The measurement stays honest on purpose. We read what the models say when asked to recommend, so the picture reflects the market’s view rather than your own marketing. Only then does the writing start, pointed squarely at where you fall short of the names that win.
Inputs
Discovery prompts
Brand knowledge
Perplexity research
SEO + traffic data
Measure · 4 AI models at once
ChatGPT
Perplexity
Gemini
Grok
after 2 human checkpoints
mentions · reasons · citations
9 reports
Share of voice
Head-to-head
Citation intel
Reasoning themes
GEO ↔ SEO
Keyword gaps
Content gaps
Exec KPIs
Prompts
Map · Intelligence
Theme normalization
raw reasons → canonical
Correlation engine
10 axes · winner patterns
Bridge themes
search intent → AI language
Make · Content
Blog Studio
compliant, AI-cited posts
Webpage Studio
pages rewritten for AI
Chatbot Studio
RAG · brand voice
What do you get?
The read and the rewrite.
A four-vendor fan-out, not one model's opinion.
Every question runs across four major AI models at once instead of just one. Asking a single model gives you that model's take; asking four shows you where they agree, where they split, and which names keep surfacing no matter who you ask. That spread is the difference between an anecdote and a read.
A recommendability judge that scores the why.
Each recommendation is scored on the things that actually move an AI's choice: how clearly you answer the question, the reasons it gives for picking a name, the angles it leans on, and the patterns shared by whoever wins. You get a structured read on why you place where you do, not a single vanity number.
Two human pauses, by design.
Nothing ships on autopilot. The workflow stops twice for a person to review: once on the strategy the data points to, and again on the finished content before anything is final. The system does the heavy reading and drafting; a human signs off on the direction and on every word that goes out.
A layered compliance defense.
Before content is final it passes a four-tier compliance check, not a single rule. Each tier catches a different class of risk, so a sensitive claim gets flagged and corrected early rather than slipping through to a published page.
A Content Lab that writes the fix.
The read is only half the job. The Content Lab writes the web pages and blog posts aimed at the exact gaps the analysis found, mapped to the questions buyers actually ask AI. Most tools stop at telling you what is wrong; this one writes what is missing.
Can you trust the read?
Yes, because the measurement is kept clean and every written claim traces back to a source.
The read reflects what the models say when asked to recommend, not language we coached into them, so it shows the market’s perception of you rather than your house marketing. That is what makes it worth acting on.
The content that fixes the gaps holds to the same standard. Every claim the Content Lab writes points back to a real source, so the pages and posts that close the gaps stand up to the same scrutiny as the read that found them.
Where to next?
Business Intelligence is the read-and-write engine behind your AI Visibility. The flagship turns your site into something AI can cite and trust; this is the system that measures how AI recommends you and writes the content to improve that answer.
See how AI recommends you today.
A walkthrough runs your real category questions through the four-vendor fan-out and shows you the language behind every recommendation, live against your own market.