namoria
AI visibility, measured
Be cited. Prove it.

Your buyers ask AI. Is it answering with your name?

Namoria measures how often your brand appears in AI-generated B2B shortlists across controlled buying-intent benchmarks — then tests which interventions may move the result.

This is a research waitlist, not an application for a paid engagement. No benchmark or commercial deliverable is promised at this stage.

Repeated across ChatGPT, Perplexity and Gemini · dated outputs · model variance tracked

01Brand X · martech

49 buying-intent queries tracked · ChatGPT · Perplexity · Gemini
39
source: namoria weekly run · week 28 2026 · 3 engines

39 of 49 buying-intent queries. Cited zero times.

Buying queryComparable competitors cited
top content marketing platformsahrefs, airtable, brafton, buffer
best conversion rate optimization toolsab tasty, convert, convertbox
best AI writing tool for marketing teamsahrefs, anyword, birdeye, canva
best referral marketing softwareambassador, captiv8, cello, dub
best event marketing softwareattendease, audience republic, audienceview, bizzabo
… and 34 more

Cited in 9 of 49 tracked queries. Absent from 39 where a comparably sized competitor already appears.

Dated output from a controlled buying-intent benchmark. Individual responses may vary; directional patterns are evaluated through repeated observations.

02Share of Model™

Share of Model™ is Namoria's benchmark metric for how often a brand appears across a fixed set of buying-intent prompts under defined test conditions. It is a controlled benchmark — not a claim about every buyer, account or AI session.

Results are reported by engine and date. Repeated observations are used to assess whether a movement is larger than normal model variation.

You do not read a dashboard to get it. You receive it, dated, with the prompts and the competitors named alongside you.

you21
market25
illustrative

03Method

1

Measure repeated, versioned buying-intent benchmarks

49 controlled buying-intent benchmarks, repeated weekly across ChatGPT, Perplexity and Gemini under versioned test conditions. Every brand named in every answer is recorded, by engine and date.

2

Intervene up to three prioritized actions

Up to three prioritized source, content or entity actions — schema.org markup, content that answers the benchmark directly, third-party placements the engines already trust — applied under a controlled protocol so their effect can be measured against variance.

3

Evaluate movement vs. variance, controls where feasible

Post-intervention movement is compared with observed variance and controls where feasible. The next runs do not automatically prove causality. They show whether the observed movement is strong enough, persistent enough and sufficiently differentiated from normal variance to merit a decision.

04Status

product research
Paused · no paid engagement offered
  1. How AI systems form B2B vendor shortlists
  2. How brand absence can be measured, and with what confidence
  3. Which evidence is useful enough to support a business decision
  4. Which operating model can deliver that evidence end to end

Paid engagements are paused while we validate the product, scope and operating model. Nothing is sold through this site.

05FAQ

How is this different from GEO tools?

GEO tracking tools give you a counter to interpret. Namoria's work is the measurement protocol itself: controlled buying-intent benchmarks, repeated across engines, with model variance tracked and outputs dated. What that measurement is worth commercially is precisely what this research phase is meant to establish.

Which engines do you track?

ChatGPT, Perplexity and Gemini — the three engines your buyers are actually using to research vendors. Each engine is benchmarked with controlled buying-intent prompts, and results are reported by engine and date.

Can I buy something today?

No. Paid engagements are paused while we validate the product, scope and operating model. You can join the research list — no report, deliverable or response time is promised at this stage.

Do I need to change my tracking or analytics stack?

No. Benchmark runs happen outside your stack — nothing is installed, connected or modified on your side.

Is the Share of Model™ number comparable across weeks?

A repeated, versioned sampling protocol helps distinguish directional brand movement from normal model variance. Results are reported by engine and date, and movements are assessed against repeated observations rather than a single run.