RAIDAR.Content analyses any page against your brand’s LLM performance data and delivers two things: text you can publish today, and structured briefings for every gap your team can’t fix alone.
The Gap · 01
After a RAIDAR analysis, you know exactly where your brand underperforms in AI search — which attributes fall short, which failure modes dominate, and how far behind the benchmark you sit. What you don’t have yet: the specific editorial moves. Which paragraph to rewrite. Which FAQ block to add. Which brief to hand off to your design team.
That’s the gap RAIDAR.Content closes. It turns your data into a prioritised, page-by-page action list — without the guesswork.
What you get · 02
For every page you analyse, RAIDAR.Content returns two types of output — covering everything from immediate copy fixes to team-ready briefings for gaps that go beyond text.
Every recommendation comes with the original text, the optimised version, the RAIDAR KPI gap it addresses and a clear rationale tied to LLM visibility. Your editor picks it up and applies it directly — no interpretation required, no blank page.
Outcome: your content team stops guessing and starts shipping.
REWRITESome visibility gaps go beyond copy. A missing expert quote. An infographic your competitors are getting cited for. FAQ blocks that answer the exact questions LLMs are being asked. RAIDAR.Content identifies these gaps and produces briefings structured well enough to hand directly to your design, video, PR or tech team.
Outcome: a single analysis turns into a sprint plan — every measure mapped to a specific failure-mode pattern in your RAIDAR data.
RECREATEThree failure modes. Five citation mechanisms. Each recommendation is classified against your brand’s RAIDAR gap profile — so you know not just what to fix, but which lever it pulls on which AI engine.
Outcome: you don’t just know what to fix. You know what each fix is worth — and where.
Across analyses, RAIDAR.Content surfaces recurring patterns: issues that appear on most of your pages, tone inconsistencies, gaps in expert positioning. The more you use it, the sharper the priorities become.
Outcome: your optimisation strategy evolves from reactive to systematic.
The Engine · 03
Every client starts with a compact overview: an LLM Fitness Score benchmarked against the competition, sentiment and brand-attribute performance at a glance, and a direct entry point into page-level analysis. From here, one click takes you into the content engine.
Zoom-ins · 03.1
Aggregate across 20+ KPIs: mention frequency, brand-attribute ownership, sentiment distribution, AIDA performance and persona visibility. Comparable over time — you see whether interventions are working.
How often is the brand mentioned in AI answers — and in what tone? Per-competitor comparison, with every gap linked directly to the pages where RAIDAR.Content can intervene.
Per attribute, the direct comparison to the benchmark — e.g. certification expertise 84% vs. 81%, digital learning offering 44% vs. 58%. Clear gaps become a clear roadmap.
Automatic issue detection based on the KPIs: where the gap sits, which failure mode dominates, which mechanism helps. RAIDAR.Content translates the diagnosis into prioritised actions.
Scrape a live page, upload protected drafts — or combine both when planned changes need to be validated against the live state.
URL analysis for live pages via Playwright. Screenshot upload for login-only or print material. Hybrid for maximum depth — visual analysis plus scraped context set.
From the overview dashboard, one click into the page-level deep analysis — with an optional editorial instruction RAIDAR.Content factors into the analysis.
Analysis paths · 04
Playwright scrapes the live page — text, headings, images, structured data — and feeds it into the analysis. Best for any published page you can access with a browser.
For pages behind login, unpublished drafts or print materials. The engine reads it visually — no live access required.
Screenshots plus the source URL: visual analysis and scraped live context together. Maximum depth for pages where planned changes need validation before you publish.
What’s behind it · 05
The recommendations RAIDAR.Content produces aren’t generic AI suggestions. Behind every output sits OH-SO’s full GEO/AEO methodology — the same framework our strategists apply in client engagements, encoded directly into the engine. Grounded in your brand’s actual RAIDAR KPI data. Classified against three LLM visibility failure modes and five citation mechanisms. Prioritised so your team knows exactly where to start.
You don’t just know what to do. You know which lever moves which needle — on which AI engine.
What it replaces · 06
The briefing your strategist would normally spend a morning writing. The 40-point GEO checklist applied manually to every page. The translation layer between “your Discovery Gap is 72%” and “here’s the paragraph to rewrite.” The back-and-forth between strategy and editorial to agree on what actually needs to happen.
RAIDAR.Content handles all of it — in a single analysis, grounded in your brand’s data, prioritised by impact.
What’s next · 07
RAIDAR.Content starts with a single URL. But the engine is built for scale.
Drop in a sitemap and get an LLM-readiness score for every page on your domain — ranked by impact, filtered by failure mode. Upload a draft before it goes live and get a pre-publish check. Ask the engine not just “how do I fix this page?” but “which pages am I missing entirely?” — and get content blueprints based on the whitespace your RAIDAR data has already mapped.
What you’re running as a focused tool today is the foundation of a full content intelligence layer — one that connects RAIDAR measurement directly to every editorial decision your team makes.
Clients
FAQ · 08
Straight answers on method, failure modes, analysis paths and what sits behind RAIDAR.Content.
RAIDAR.Content is the Content Intelligence Engine — an analysis tool that takes a URL or screenshot of any web page, runs it against your brand’s RAIDAR performance data, and returns two types of output: paragraph-level text rewrites (REWRITE) and structured briefings for gaps beyond copy (RECREATE). It’s the bridge between your RAIDAR analysis and your content team’s to-do list.
Classic SEO tools return abstract pointers: “improve your meta description”, “add more headings”. RAIDAR.Content returns finished copy suggestions — original text, optimised version, RAIDAR rationale, fact-check status. And where copy isn’t enough, it produces briefings for your design, PR and tech teams. The difference: actionable output, not a checklist to interpret.
Two types of output. REWRITE outputs are paragraph-level, copy-ready text optimisations — sorted by priority (P1–P3), with original text, optimised version, the RAIDAR KPI gap addressed and a GEO/AEO classification. RECREATE outputs are structured briefings for actions beyond text: image concepts, video ideas, expert-quote direction, FAQ blocks, schema recommendations. Both are ready to hand off immediately.
Three. Discovery Gap — the page isn’t recognised as a relevant source for a category. Authority Gap — the brand is mentioned but not cited first, credibility signals are missing. Sentiment Gap — the brand is cited, but without conviction or positive framing. Every recommendation maps to one of these.
URL analysis for any live, publicly accessible page. Screenshot analysis for pages behind login, unpublished drafts or print. Hybrid for maximum depth — when you want visual context plus scraped live data combined.
Three context layers feed into every analysis: your client profile (audiences, goals, specific requirements), your briefings (tone of voice, core messages, forbidden terms) and aggregated insights from previous analyses (recurring patterns, systemic issues across your pages). The more analyses you run, the more precisely the engine understands your brand.
Currently German and English, with automatic language detection. Recommendations are written in the language of the analysed page.
Every recommendation carries a fact-check status. The engine includes quality controls for language accuracy, factual plausibility and brand consistency — flagging anything that requires human verification before publishing.
Built by OH-SO Digital — a team of European AdTech and MarTech specialists. OH-SO has been measuring and optimising brand visibility in digital environments since the early days of programmatic. RAIDAR.Content is the first productised output of that experience applied to the AI era.
15 minutes. One URL. A first content analysis.