From Monitoring Data to
Published Articles

Source analysis — find where AI gets its information ($3)

Before writing content, you need to know where to publish it. Source analysis examines all platforms cited across your monitoring results and identifies three categories: content platforms (blogs, media outlets, wikis), review platforms (G2, Trustpilot, Capterra), and marketplace platforms (Amazon, industry directories).

Each platform shows which AI engines cite it, how frequently, and what type of content performs best there. Missing platforms — where competitors are present but you’re not — are highlighted as immediate opportunities. Publish content on the platforms AI actually trusts, not where you assume it looks.

Topic generation — 7 content types calibrated to your data ($3)

AI generates topics based on your monitoring results, not generic keyword research. Each topic targets a specific gap or opportunity identified in your latest monitoring data.

7 topic types cover different content strategies: HowTo guides (step-by-step instructions AI loves to cite), comparisons (your product vs competitors AI already mentions), listicles (ranked lists matching how AI structures recommendations), case studies (real results that serve as evidence), thought leadership (original analysis AI can’t generate itself), news (timely content for recency-sensitive engines), and FAQs (direct answers to the questions your monitoring tracks).

Each generated topic includes a target platform, suggested length, key points to cover, and which monitoring queries it addresses. You’re not guessing what to write — each topic connects directly to a measurable gap in your AI visibility.

Article writing — GEO-optimized, ready to publish ($6)

Articles are written with specific GEO signals that make AI engines more likely to cite them. This is not about keyword stuffing — it’s about structural patterns AI models are trained to extract and reference.

• Answer-first format — the key answer appears in the opening paragraph, matching how AI extracts information
• Entity mentions — proper nouns, brand names, and industry terms are used consistently so AI can build accurate entity associations
• Structured data patterns — headers, lists, and tables organized for AI parsing, not just human readability
• Citation-worthy passages — self-contained paragraphs that AI can quote directly without losing context
• Comparison sections — fair competitive analysis that positions your brand as a credible recommendation

Articles export as Markdown, ready to publish on your blog, CMS, or target platform. Each article references the monitoring data and platform analysis it was built from.

Reference materials — ground articles in your real data

Upload reference files (PDF, DOCX, XLSX) before generating an article. The system extracts key facts, statistics, product specifications, and quotes from your materials and weaves them into the article. Instead of generic industry claims, the article cites your actual data — making it more specific, more credible, and more likely to be cited by AI engines.

Use case examples: upload a product spec sheet to ground a comparison article in real specs, upload a case study PDF to generate a thought leadership piece with verified results, or upload competitive research to inform a positioning article.

Target publication — match tone and format automatically

Select a target publication before writing. The system automatically profiles the platform’s editorial guidelines — typical article length, tone of voice, formatting conventions, header style, and content preferences. The generated article adapts to match, so it reads as if it were written for that specific platform.

This goes beyond generic “professional tone” adjustments. A guest post for a tech blog will have a different structure than a thought leadership piece for a business magazine or a listicle for a review platform. The system handles these differences automatically.

How does niche platform discovery work?

Source analysis doesn’t just find mainstream platforms — it discovers industry-specific sites that AI engines cite for your particular niche. These are the specialized directories, trade publications, professional forums, and vertical marketplaces that generic SEO tools overlook but AI engines actively reference when answering niche queries.

Each discovered platform includes actionable context: what type of content performs best there, which AI engines cite it most frequently, and whether your competitors are already present. This turns content strategy from “publish everywhere” into “publish where AI actually looks.”

Full pipeline: $12 from analysis to published article

The three stages can run independently or as a connected pipeline. Source analysis ($3) identifies the right platforms. Topic generation ($3) creates data-driven content ideas. Article writing ($6) produces ready-to-publish content. Total: $12 for a complete content piece informed by real monitoring data.

You can run any stage separately. Already know where to publish? Skip source analysis and generate topics for $3. Have your own topics? Go straight to article writing for $6. The pipeline is flexible — use the stages that fit your workflow.

Close the loop with AI Traffic Pixel

After publishing, install the AI Traffic Pixel on your content pages to track what happens next. The pixel identifies visitors from all 8 AI engines and traces them through to conversion: which article brought the visitor, which AI engine sent them, and whether they converted.

This closes the content ROI loop. You see which articles actually drive AI traffic, which topics generate the most revenue, and which platforms deliver the highest conversion rates. The next content cycle uses these results to double down on what works.