AI content workflows, the content grid that runs itself.

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.

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.

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.

Columns come in five families: data, reference, execution (LLM, Perplexity research, web fetch, scrape, Google search, CMS push), Mentionova-native (citation check, competitor overlap, Search Console sync, prompt sync), and formulas, so a grid both generates and verifies content.

Your workspace brand-voice profile, guidelines and real content examples, is injected into every LLM column, so drafts across the grid read in one consistent voice.

Yes. Cost is tracked per cell and per run, and a cost cap stops a run before it overspends, so large grids stay predictable.

Grids run citation checks natively, every LLM-drafted row is tested across the six AI engines before it publishes. Topics where your brand is already cited get prioritized, and brand-voice injection keeps every output aligned with how models already describe you.