Modern AI · Modern Discovery

AI Visibility
Intelligence
Deep-Dive.

A full-stack view of what Modern Discovery measures, what enterprise brands see in the platform, where they lose ground in AI-generated recommendations, and what they gain by closing that gap.

Live Platform Shortlist Share AVI ASOV

Confidential · Modern AI Inc. · 2026

01 / 14 · Modern Discovery Deep-Dive

The Problem

Discovery shows companies what AI tells customers about their brand, and what to do about it.

80%

of URLs referenced by AI models do not appear in Google's top 100 organic results.
Ahrefs + BrightEdge, 2024

26%

of brands have zero mentions in Google AI Overviews. Invisible at the moment of recommendation.
Ahrefs, 2025

Traditional SEO tools were not designed to measure AI visibility. A brand can rank first in Google and still be absent from every AI recommendation. That gap is where buying decisions are made.

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The AI Search Collapse

SEO in AI search has collapsed 60-80% in the past two years.

In early 2024, around 76% of pages ranking in Google's top 10 were also cited by AI engines. By February 2026, that overlap had dropped to between 17% and 38%. Ahrefs measured 38% across 863,000 keywords. BrightEdge's methodology produces 17%.

AI Overviews

60%

drop in organic clicks when Google AI Overview answers the query
Ahrefs, 2025

Traffic Shift

25%

projected drop in traditional search traffic by 2026 as AI surfaces absorb intent
Gartner, 2024

AI Queries Daily

2.5B

ChatGPT queries processed daily. Each one a brand recommendation moment.
OpenAI, 2025

The $33.7B GEO market (50.5% CAGR through 2034) is being built on this measurement gap. Brands that close it first hold the position.

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What Modern Discovery Measures

Three instruments. One measurement layer.

Headline Metric

Shortlist Share

The fraction of recommendation and comparison queries where your brand lands in the top position across AI surfaces. This is the question that tracks to revenue: not "do they know us" but "do they recommend us when a customer is ready to buy."

Formula: recommendation + comparison queries where brand ranks first ÷ total queries in those categories

Technical Backbone

AVI

AI Visibility Index. Composite score across all five query categories: awareness, comparison, recommendation, authority, use-case. AVI = Mention Rate (55%) + Position Score (25%) + Consistency Score (20%). Every score is anchored to source queries. CFO-grade receipt.

Scale: 0 to 100 · Methodology v2.3

Cross-Surface Presence

ASOV

Agentic Share of Voice. References across AI surfaces on commercial-intent queries measured as share across ChatGPT, Claude, Gemini, Perplexity, Google AI Mode, and Google AI Overviews. ASOV surfaces the cross-model consistency gap that AVI alone does not expose.

6 AI surfaces · Monthly cadence

Mention Rate Source Attribution Consistency Score Competitive Delta Narrative Risk Flags

AI surfaces tracked today: ChatGPT, Claude, Gemini chat, Perplexity, Google AI Mode, and Google AI Overviews.

All metrics auditable to source query. Public methodology page live at modernai.io/ai-shortlist-score/methodology. 20 queries per entity across five intent categories, six-surface panel, 0 to 100 within-niche normalization, versioned change log.

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What You Get

Measure → Diagnose → Prescribe → Automate (V2)

01

Measure

Shortlist Share, AVI, and ASOV across ChatGPT, Claude, Gemini, and Perplexity. 20 queries per entity across five intent categories. Monthly cadence. Every metric versioned and timestamped.

1,200 API calls · 6 entities · 10.5 min wall clock

→

02

Diagnose

Query Category Analysis surfaces where you lose ground and which competitors are taking the slot. Consensus gaps across models. Model-by-model breakdown. Use-case category drill-through.

5 query categories · 6 AI surfaces

→

03

Prescribe

Prescriptive GEO playbook. Every gap comes with a ranked action: content, structured data, UGC surface, or editorial authority move. Applied button captures your team's action. The platform measures actual Shortlist Share lift on the next run.

Lift measured · receipt attached

→

04

Automate

GEO content deployment. Optimized content injected into Reddit, blogs, knowledge bases, and structured data based on the gap analysis.

V2 · H2 2026 roadmap

05 / 14 · Modern Discovery Deep-Dive

Worked Example · National Brand Portfolio, #1 in Category

A National Brand. #1 in category, multi-brand portfolio.
AI knows the brand. AI under-recommends it at the moment of purchase.

1,200 model checks. Six entities. Five query categories. A map of where AI visibility ends and AI recommendation begins.

The Core Finding

Parent brand AVI 76.18. Mention rate 76.5%. Shortlist Share 12.5%. AI knows the brand; recommendation lags awareness by 64 points. This is the buying-moment gap the Insight Layer surfaces and prescribes against.

Methodology v2.3 · 1,200 model runs · six AI surfaces

06 / 14 · Modern Discovery Deep-Dive

Portfolio Scorecard · National Brand, Multi-Brand Portfolio

Six brands. One measurement run. Full picture.

Brand
AVI
Mention Rate
Shortlist Share
Parent brand
76.18
76.5%
12.5%
Sub-brand 1
76.52
78.5%
12.5%
Sub-brand 2
70.70
67.5%
18.75%
Sub-brand 3
67.37
62.5%
25.0%
Sub-brand 4
47.15
37.5%
3.75%
Sub-brand 5
0.00
0.0%
0.0%

Report IDs: rpt_d3ef9c0d, rpt_caf6d71a, rpt_7f2762ca, rpt_732563aa, rpt_8cb1657c, rpt_3ec8f55b

Key insight

The parent brand and Sub-brand 1 have strong awareness (76-78% mention rate) but only 12.5% Shortlist Share. AI knows the brand. AI defaults to a premium digital-native competitor on purchase queries.

Efficiency signal

Sub-brand 2 has the second-highest Shortlist Share at 18.75% despite a lower mention rate. High recommendation rate relative to awareness.

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Diagnosis · Gap Matrix

Where the brand wins. Where it loses. Who takes the slot.

Query Category
AI Presence
Who Wins When the Brand Loses
Awareness and Authority
94-100%
Brand owns this lane. Strong across all 6 surfaces.
Comparison queries
90-100%
Appears in every "top brands" list. No gap.
Recommendation queries
48-79%
Premium digital-native competitor ranks first. Gemini weakest at 48%.
Use-case queries
32-57%
Premium digital-native and specialty brands win specific-use queries.
Sub-brand category lanes
Near zero / Lost
Specialty competitors own organic / sustainable and seasonal lanes. Sub-brand 4 at 3.75% Shortlist Share.

Gemini powers Google AI Overviews, where most category shoppers land. When Gemini misses recommendation queries, that audience defaults to the digital-native competitor. The Insight Layer maps each category to named buyer archetypes, surfacing the gap by category rather than headline average.

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The Insight Layer · In Practice

What persona-driven queries look like across verticals.

Authority queries ask who is reputable in a category. Use-case queries ask which product solves a specific problem at the buying moment. Aggregated mention tracking treats them the same. The Insight Layer separates them by named buyer archetype and measures the gap.

Skincare
Authority "best skincare brands for sensitive skin"
Use-case "which serum actually fades hyperpigmentation without irritating"
Protein Powder
Authority "best protein powders for women over 40"
Use-case "which protein powder doesn't taste chalky in smoothies"
Mattresses
Authority "best mattresses for back pain"
Use-case "which mattress doesn't sleep hot for a side sleeper"
Pet Food
Authority "best dog foods for sensitive stomachs"
Use-case "which puppy food is AAFCO certified and grain-free"

20 queries per entity · 5 intent categories per query set · named buyer archetypes per category

09 / 14 · Modern Discovery Deep-Dive

Competitive Landscape · GEO Category

Discovery's lane. Five named alternatives. Fit, not superiority.

Capability
Discovery
Adobe LLM Optimizer
Semrush AIO
Profound
Goodie AI
Otterly.ai
Persona-driven query architecture
Yes
Not documented
Not documented
Not documented
Not documented
Not documented
Stack independence (no suite required)
Yes
Suite-strong
Inside Semrush
Yes
Yes
Yes
Published annual pricing (quoteable in the meeting)
Yes
Custom quote
Starter/add-on public; Enterprise custom
Tiered + custom
Demo-led
Yes
Open methodology page (publicly inspectable)
Yes
Not as audit artifact
Not as audit artifact
Not as audit artifact
Not as audit artifact
Not as audit artifact
Three white-label reseller tiers, fixed-rate
Yes
Not documented
Not documented
Agency program
Agency workflow
Agency Partner
Redundant by design (across production models)
Yes
Not documented
Not documented
Not documented
Not documented
Not documented

Fit, not superiority. Adobe LLM Optimizer fits brands inside Adobe Experience Cloud. Profound serves enterprise prompt-panel scale. Goodie covers broad surfaces. Otterly fits SMB and pitch-workspace motion. Discovery is the choice when measurement needs persona-driven query architecture, public methodology, and independence from any execution suite.

10 / 14 · Modern Discovery Deep-Dive

Differentiation

Why Discovery.

01

Insight Layer · persona-driven query architecture

Five intent categories mapped to named buyer archetypes per category. The Insight Layer surfaces the buying-moment gap: authority queries where the brand wins category knowledge versus use-case queries where the brand loses the purchase decision. Aggregated mention tracking cannot see this gap. The persona-driven design is built to see it and to prescribe against it.

02

Named category-defining metrics

Shortlist Share and AVI measure recommendation presence when the buyer is deciding.

03

Source-receipted scores

Every score anchored to raw AI responses. Trace any number to the query that produced it.

04

Redundant by design

Every measurement runs across six AI surfaces, including four LLM-chat models and the two Google AI surfaces. The measurement program is designed to stay resilient when any single model vendor changes terms or has an outage.

05

Closed prescription-to-lift loop

Applied button captures team actions. Platform measures actual Shortlist Share lift on the next run.

Published methodology. Open audit.

Public methodology page live at modernai.io/ai-shortlist-score/methodology. 20 queries per entity, five intent categories, immutable versioned, six-surface panel, 0 to 100 within-niche normalization, versioned change log.

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Operator View · What You Get

A versioned intelligence report. Every metric receipted.

Modern Discovery Intelligence Report. Illustrative synthetic data.

The Intelligence Report surface inside cloud.modernai.io. Shortlist Share, AVI, ASOV, and Consistency front and center. Each number traces to source queries. Illustrative synthetic data.

Headline scores + drill-through

Shortlist Share, AVI, ASOV across six AI surfaces. Five-category query drill, sorted by gap.

Competitive displacement

Named competitors who take the slot. One digital-native competitor displaced the brand across 5 of 6 entities.

Prescriptive playbook

Ranked action list. Applied button. Lift measured on next run.

20

queries per entity

6

AI surfaces per run

Dashboard live at cloud.modernai.io. Versioned methodology. Source receipts on every score.

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Pricing · Annual Contract

Entity-based. Annual only. Publicly priced.

Core

$15K

per year · up to 3 entities

  • AVI + Shortlist Share + ASOV + Source Attribution
  • ChatGPT, Claude, Gemini, Perplexity, Google AI Mode, Google AI Overviews
  • Dashboard + monthly scan cadence
  • Published methodology

Add-on entity: $3K/yr each

Growth

$42K

per year · up to 15 entities

  • Everything in Core
  • Portfolio-level competitive mapping
  • Multi-entity scorecard view
  • Prescriptive GEO playbook + applied lift

Most common enterprise entry

Enterprise

Custom

annual contract · up to 30 entities

  • Everything in Growth
  • Executive briefings
  • Multi-year contract + implementation support

Talk to us · info@modernai.io

Annual contracts only

2-year 10% off. 3-year 15% off.

Included at every tier

6-surface coverage, open methodology, dashboard, monthly scan, AVI + Shortlist Share + ASOV.

Agency reseller program

Three white-label tiers. Fixed-rate, no pass-through. Redundant across six surfaces.

13 / 14 · Modern Discovery Deep-Dive

Start Here

We run the scan on
your brand before the call.

Every Discovery engagement starts with a run on your brand and your top competitors. You see your Shortlist Share, AVI, and the exact queries where competitors take the slot you should own. Then we talk.

Book a demo

30 minutes. Your data in the room.
No pitch deck. Signal first.

Book a Demo

Read the methodology

AVI formula, query categories, source attribution,
versioning policy. Open for audit.

View Methodology

Matt Kott

Founder, Modern AI Inc. · info@modernai.io · modernai.io

Confidential · Modern AI Inc. · Modern Discovery v1.0 Deep-Dive · 2026

14 / 14 · Modern Discovery Deep-Dive