AI-optimised content, built to be cited.

Plan, write, score and publish articles that rank in search and get quoted in AI answers. Every draft starts from a prompt you're losing — and ships with citation best practice built in.

N northline.com Untitled draft New draft Export

What should we publish next?

Describe what you want to write…
Gap #7 Northline voice
Win back a lost prompt Refresh a page that dropped Answer a Reddit thread
AI readiness
0/ 100
Fix issues with Agent
0
Words
0
Headings
0
Sources
Issues to improve
high

Link lab data to the grip claims — models skip unsourced comparisons.

medium

Add Product + FAQ schema to the comparison table.

Analyzed brands that consumers trust

Plausible Beehiiv Tally Yotpo Loops

One workspace, from gap to citation.

The Content Engine reads your brand context, prompts, competitors, sources and AI visibility data — then plans, writes, scores and tracks every piece.

01 · Plan

Briefs built from the gaps you’re losing

Pick a prompt where a competitor gets cited and you don’t. Plan mode turns it into an execution-ready brief — audience, angle, structure, sources to beat and open questions — before a word is written.

Prompt gap#7 of 112
“best trail running shoes for beginners”
ChatGPTMissing PerplexityMissing ClaudeMissing GeminiCited #3 GrokCited #5
Cited instead: trailreview.co, outfitterguide.com
BriefApprove & write
AudienceNew runners moving from road to trail AngleFit and confidence over spec sheets Sources to beat2 review sites, 1 retailer · cited 11× StructureQuick answer · comparison · fit guide · FAQ Open questionWear-test data for the Ridge 3?
02 · Write

Research-backed drafts, in your voice

The Engine studies the pages models already cite, then writes with your knowledge base and first-party data. Every claim is footnoted, and the Agent revises any passage you select.

Studied38 pages · 2 docs
trailreview.co/beginner-shoesCited 7×1
outfitterguide.com/learn/trailCited 4×2
Northline wear test 2025.pdfKnowledge base3
Ridge 3 product specsKnowledge base4

What matters on your first trail

Most first-time trail runners overbuy. Review sites push aggressive lugs1, but groomed trails rarely need more than 4–5 mm2. In Northline’s 2025 wear test, 7 in 10 beginners preferred a 6 mm drop over a 4 mm one3 — it eases the switch from road shoes.

Agent Add the sample size (240 runners) so models can quote this as a statistic. ApplyRevise

The Ridge 3 pairs that drop with a 4 mm lug4, so it handles the road section to the trailhead without feeling…

03 · Score

Know it’s citable before you publish

An AI readiness score checks the eight things models look for — and tells you exactly what to fix before anything goes live.

64First draft
92
Ready to publish
Structure96
Depth90
Citations78
Link the lab data behind the grip claims.
Source coverage88
Schema100
Internal links84
Readability94
Overlap riskLow
<article>
<h1>The best trail running shoes for beginners</h1>
<section id="quick-answer">…</section>
<table>…</table>
</article>
<script type="application/ld+json">
{ "@type": "FAQPage",
"mainEntity": [{ "@type": "Question",
"name": "Can I use trail shoes on the road?" … }]}
</script>
04 · Publish

Ship clean, structured pages

Edit in a full rich-text canvas, then export clean HTML, markdown, rich text or .docx. FAQ and Product schema travel with every export, so the structure models read survives your CMS.

Semantic headings, no inline styling JSON-LD schema generated from the draft Readiness score stays attached to the page
05 · Prove

See the moment AI starts citing you

After you publish, BeonAI keeps running the target prompts every week across five models — and ties AI-referred sessions back to conversions in GA4.

47%+29.0
Share of voice on target prompts
38
Conversions from AI-referred sessions
Citation trackerCitedNot yet
“best trail running shoes for beginners”
W1W2LiveW4W5W6W7W8Since ChatGPTW5 PerplexityW3 GeminiW6 ClaudeW8 Grok—
4 of 5 models now cite the guide — Perplexity first, one week after it went live.
3.4×
more AI citations than a team's previous content
19 days
median time from publish to first citation
−75%
time from brief to publish-ready draft
5
AI models tracked weekly — ChatGPT, Perplexity, Gemini, Claude, Grok

Who the Content Engine is built for

Built for every team that owns AI search.

Shoppers ask ChatGPT which brand to buy before your site ever loads. The Content Engine finds the ‘best [category]’ prompts where rivals get recommended, and writes the buying guides that put your products in the answer.

Buying guides built around ‘best [category]’ promptsSee which review sites models trust over your PDPsTie AI-referred sessions to revenue in GA4
Example gap
“best running shoes for flat feet”Rival recommended

Teams that stopped guessing.

14 clients · 1 workflow
“Every client has its own voice and competitor set, and the monthly citation report basically sells the renewal for us.”
SSaniaCEO · Fieldwork Studio, marketing agency
0 → 4 of 5 models citing
“We finally have a content plan tied to pipeline. The readiness score settles every ‘is this good enough?’ debate before review.”
SStacyHead of Growth · Kestrel Labs, B2B SaaS
+212% AI-referred revenue
“ChatGPT was recommending three of our competitors for our hero category. Six buying guides later, we're the first brand it names.”
AAlanCMO · Harbor & Pine, consumer brand

Built for AI search from day one.

Ahrefs and SEMrush bolted AI onto SEO. Profound measures AI visibility, then stops. BeonAI connects the gap, the draft and the citation in one loop — so teams get proof, not just output.

Capability Ahrefs SEMrush Profound BeonAI
Built for AI visibility
Tracks ChatGPT, Perplexity, Gemini, Claude and Grok
Citation source intelligence
Briefs generated from your visibility gaps
Writes in your brand's voice
AI readiness score before publish
GA4 + Search Console traffic analytics
Tracks when published content gets cited

Based on publicly available feature information as of October 2026. Coverage may vary by plan.

Frequently asked questions.

Still deciding? Book a 20-minute walkthrough on your own prompts.

Other writers start from a blank prompt. The Content Engine starts from a prompt you're measurably losing in AI answers, studies the exact pages being cited instead of you, writes to beat them in your voice — and then tracks whether it worked.

Pick a gap, and plan mode builds a brief: audience, angle, structure, sources to beat and open questions. Approve it, and the Engine drafts section by section, scores AI readiness, and flags anything to fix before review.

Yes. Every draft opens in a full rich-text canvas. Edit directly, or select any passage and ask the Agent to revise it. Review states keep approvals clear across the team.

Yes — your published archive, knowledge base, brand voice guidelines, competitor intel, industry news, GA4 and Search Console all feed the brief and the draft.

Markdown, HTML with schema, rich text and .docx, or send directly to your CMS. Structured data like FAQ and Product schema travels with the export.

BeonAI runs your prompts across ChatGPT, Perplexity, Gemini, Claude and Grok every week and archives what gets cited. The readiness score is built from those patterns — structure, depth, sourcing, schema and more.

Yes. Each brand or client gets its own prompts, competitors, voice profile and content library, with the same workflow and reporting across all of them.

Make your next article the one AI cites.

Start with the prompt you're losing most. Get a planned, scored, publish-ready draft the same day.