A content grid is a spreadsheet where every column can execute work: LLM drafts, citation checks, brand-voice tests, and Search Console data, all in one table, automated on a schedule. Use it alongside AI content optimization to turn visibility gaps into published, cited content without manual hand-offs.
A content grid is a spreadsheet where columns execute work, LLM prompts write drafts, citation checks verify each row against six AI engines, and brand-voice injection keeps every cell on-message. Drop in topics; get drafted, checked, on-brand content out.
Add topics to the first column, then attach column types: LLM Draft for copy, Citation to check which engines cite you, Brand ✓ for voice alignment. One row = one piece of content, built end-to-end in a single table.
Hit run. LLM columns draft every row; citation columns test each output across six AI engines; brand-voice columns score the result. Cost is tracked per cell. A cap stops the run before it overspends.
Wire a cron trigger to the grid run, add a branch for conditions, route the output back to the grid or fire a notification. Every week the grid re-runs, new drafts, fresh citation checks, same brand voice, without anyone clicking a button.
Each column can be an LLM prompt, a web search, a scrape, or a Mentionova-native check, citation status, competitor overlap, Search Console clicks, so a row goes from topic to drafted, fact-checked and scored without leaving the grid. Use the grid alongside AI visibility tracking to prioritize which topics to draft first based on live citation data.
A visual builder wires triggers to channels to outputs, run on a cron, pull metrics, branch on a condition, and save the result to a grid or fire a notification. The busywork runs while you sleep. Pair automations with AI brand monitoring to trigger drafts automatically when your visibility changes.
Your brand-voice profile, guidelines plus real content examples, is injected into every LLM column, so a hundred drafts read like your team wrote them, not a hundred different robots.
A visual builder connects a trigger, scheduled, webhook, manual or API, to channels like LLM or web search, then routes the output to a grid, a notification, or a CMS push. The whole sequence runs unattended so recurring content work happens on a clock, not a to-do list.
Data, reference, execution, Mentionova-native and formula.
Citation check, competitor overlap, GSC sync, prompt sync.
Triggers, channels, logic and output nodes.
Per-cell and run-level cost tracking with a cap.
Drop a list of topics in the first column. The LLM columns draft, the citation-check columns verify, and the brand-voice profile keeps every cell on message, the whole thing fires on a schedule while you're doing something else.
Topics in → drafted, checked, on-brand rows out
LLM columns are executable grid columns that send each row's topic to a large language model. Write one prompt; the column runs it across every row and writes the output back, draft, summary, structured data, or anything else, so one instruction scales to hundreds of pieces. Every LLM column inherits your shared brand-voice profile automatically.
Enter your domain and we'll show you the AI visibility gaps worth a grid, then you can run the drafts, checks and publishing from one table.
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