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.
Takes ~3 minutes · no credit card