Advisory tells you what's broken. We hand you the fix.
Every recommendation in CiteAgentic ships with a 1500-word AI prompt - full context, deliverable spec, output format - that drops straight into Claude Code or ChatGPT. The output is publishable content for your repo.
What's wrong with AEO tools that only give advice?
You run an audit. It finds 47 issues. It tells you what each one is, why it matters, and which severity it has. You click "export PDF". You read the PDF on Tuesday. You forward it to your engineer on Wednesday. They read it on Friday. Nothing ships.
Every monitoring SaaS in this category - HubSpot AEO, Profound, AthenaHQ - solves the detection half of the problem. Detection is the easy half. Detection has been a solved problem since 2010. Google's Search Quality Evaluator Guidelines have always emphasised helpfulness - but helpfulness requires action, not just insight.
The hard half is action. Going from "I see the problem" to "I shipped the fix" is where 80% of the value lives, and where every other tool quits.
How does the CiteAgentic fix-prompt loop work?
CiteAgentic's recommendations are designed for one specific motion:
- Open the dashboard. Top recommendation is right there.
- Click "Copy AI prompt". A 1500-word prompt with full brand context, competitor names, exact lost prompts, sample answers from each engine.
- Paste into Claude Code (or Cursor, or ChatGPT, or whatever your team uses).
- Press enter. Claude reads the prompt, opens the relevant files in your repo, and produces the deliverable.
- Review, edit, ship.
Total time: 90 seconds of dashboard time, then however long the AI takes to do the work (~5 minutes for a 1500-word blog post).
That's the loop the entire product is designed around.
What a fix prompt actually contains
For a competitor_wins recommendation:
- Brand context - name, domain, every variant your brand goes by, B2B/B2C, category.
- Competitor identity - name, domain, name variants.
- The exact prompts you lost - full text of each, the engine that answered, and a 280-character snippet of how the competitor was framed in that answer.
- Deliverable spec - "1200–1800 word markdown blog post with question-form H1, lead answer in first 50 words, Hearst-style enumeration, sourced external citations, JSON-LD Article + FAQPage."
- Output format - fenced code blocks, file path suggestion, JSON-LD as a separate fenced block ready to paste into
<head>. - NEEDS_FACT convention - facts the AI can't verify get marked for human review instead of fabricated.
It's not advice. It's the actual command to do the work, with everything pre-filled. About 1500–2000 characters per prompt.
Why this is hard for competitors to copy
Three reasons we think this stays a moat for a while:
- Prompt-engineering DNA - we've spent 18 months running an audit pipeline (SEO, AEO, security, perf, messaging) where every finding ships with a paste-ready fix prompt. We know how to write them. New entrants don't.
- The data context - the prompt is only useful because of the surrounding data (competitor identity, lost prompt texts, sample snippets, brand variants, audit findings). A monitoring tool without the audit pipeline can't easily generate it.
- The shipping discipline - every new feature in the platform requires a paired fix prompt. We turned this into a memory rule and saved it. Future features inherit it. Competitors retro-fit; we ship into it.
The principle, distilled
Every recommendation, finding, and rec-like surface ships with a copy-pasteable AI fix prompt. Both advisory (the "why it matters") AND actionable (the prompt). Never advisory-only.
That's the rule. It survives every product phase. It's the moat.
See it in your own dashboard.
Run a scan, open the recommendations tab, click any rec - copy the prompt, paste it into your AI workflow. The first one's free.