RAIDAR is a suite of independent products covering the AI marketing chain. Brand teams get high-definition LLM insights, optimized journey flows, and citation-grade assets that feed the models.
3 products · 3 live
Full visibility into how every LLM describes, cites, and positions your brand, working across markets, segments, and the full AI stack.
Continuous journey-check on how customers experience every page and viewport. Read end-to-end like a customer, scored like a designer.
Source-level view of which assets the models cite, which surfaces they reach, and where authority leaks. Content stops being a guess.
The RAIDAR Suite
RAIDAR measures across the surfaces that decide whether a brand wins or loses today. Each layer runs continuously, each layer informs the others.
How AI assistants describe, cite, and place your brand across every model.
RAIDAR.GEOWhich assets the models cite, which surfaces they reach, where authority leaks.
RAIDAR.ContentContinuous accessibility and journey checks — read like a customer, scored like a designer.
RAIDAR.UXPerformance, structured data, crawl reachability — the technological foundations for LLM readyness.
RAIDAR.Tech · plannedClients
Approach
Workshop with your team to set the territory: brands, segments, markets, journeys, and assets that matter. Each product gets calibrated to your reality before the first scan runs.
RAIDAR measures continuously, not once. GEO probes the LLM layer. UX probes the journey layer. Content probes the asset layer. Every finding is parsed, scored, and placed in context — reproducible, every time.
You get an interactive map per product, ranked by impact. Filter by segment, market, surface, or layer. Use the snapshot to brief a sprint, or the live feed to run an operating cadence. Run it yourself, or run it with us.
Steer how AI shapes your brand, how your site converts, and what content the models cite.
FAQ
Honest answers on scope, methodology, integrations and what happens to your data.
RAIDAR is a suite of independent products built for marketing teams operating in the AI era. Three instruments cover the marketing chain: RAIDAR.GEO measures how LLMs describe, cite, and position your brand. RAIDAR.UX measures how customers experience your site, end-to-end. RAIDAR.Content measures which assets the models actually cite and where the gaps are. Each product runs on the same underlying logic: continuous scans, statistically validated, ranked by impact. Use one. Use all three. They work either way.
No. Each product is independent and stands on its own. Brand teams typically start with RAIDAR.GEO to map their AI visibility, then add RAIDAR.UX when journey performance becomes the next bottleneck, and adopt RAIDAR.Content once the asset layer needs systematic attention. The suite is designed for stepwise adoption, not for a forced bundle.
Because the surfaces that decide brand performance today don't operate in isolation. The way LLMs describe you depends on the content that feeds them. The way customers experience your site depends on what AI told them before they arrived. RAIDAR keeps the measurement consistent across all three layers, same methodology, same evidence standard, same operating logic. That's the difference between three vendors and one operating picture.
Every engagement begins with a workshop. We call it the Define phase. Together with your team, we set the territory: brands, segments, markets, journeys, and assets that matter. Each product gets calibrated to your reality before the first scan runs. From there, RAIDAR runs continuously and you decide on the cadence, snapshot for a sprint brief, or live feed for an operating rhythm. Run it yourself, or run it with us.
Both. You can start with a single high-resolution analysis to map your current position, useful for a launch diagnostic, a board briefing, or entering a new market. But the AI era shifts weekly: training data updates, model releases, journey changes, competitor moves. A one-time snapshot is a photo. Continuous scanning is the live feed. Most clients move from snapshot to subscription within the first quarter.
Traditional analytics measure what happens on your site: pageviews, conversions, funnels. That's still useful, but it stops at the page. RAIDAR measures the surfaces outside the page that increasingly decide whether customers ever arrive: the LLMs that describe your brand before a click, the journey moments where AI-shaped expectations meet your actual experience, the content layer that determines which assets models will cite next. RAIDAR doesn't replace your analytics stack. It measures what your stack can't see.
Brand teams operating in markets where AI is already shaping purchase decisions. That includes CMOs who need to defend brand position with evidence, product leads looking for whitespace, content leads building citation strategy, and ecom leads competing in the pre-purchase conversation. RAIDAR is deliberately built for the people who own the brand, not the people who maintain the data warehouse. No engineering ticket required.
AI outputs are probabilistic, which makes naive single-prompt or single-scan tests unreliable. RAIDAR solves this with scale and statistical discipline. Every insight is backed by large sample sizes, run across multiple models or surfaces, and repeated over time to control for drift. Multi-stage validation frameworks filter noise from signal. The methodology has been independently audited by Statista. The result: findings you can defend in a boardroom and act on with confidence.
RAIDAR is built by OH-SO Digital, a team of AdTech and MarTech pioneers from Europe. In the 1990s, they developed ADTRACTION, one of the world's first ad tracking tools. Later they built NEXT AUDIENCE, the world's first data management platform, grown out of a retargeting ad server stack that was years ahead of the market. RAIDAR isn't a feature bolted onto a generic analytics suite. It was architected from the ground up by a team that has spent three decades building the measurement infrastructure the digital industry now takes for granted. Audited by Statista, powered by WPP.