The AI Signaling instrument

Measure how AI recommends you. Then move it.

SignlLabs scores how recommendable your brand is across ChatGPT, Claude, Perplexity, Gemini, and Copilot, then engineers the lift. One number. Six dimensions. Ten buyer questions.

Maturity Method Score
5 engines · live
47
/ 100
STANDING
Foundation
72
Endorsement
41
Alignment
58
Presence
33
Warmth
61
Constancy
38
Weakest dimension: Presence. Next move: seed the sources AI retrieves from. Illustrative scorecard.
The shift

Search is being replaced by answers.

Buyers ask AI who is best and trust the first name it gives. Visibility is no longer about ranking. It is about whether the machine names you.

~2.5B
monthly users see AI-generated answers
Google / TechCrunch, 2026
8% vs 15%
clicks halve when an AI summary appears
Pew Research, 2025
-33%
Google referrals to publishers, to Nov 2025
Chartbeat / Reuters
89%
of AI citations from outside the top 100
BrightEdge, 2026
The Maturity Method™

Six dimensions. One score, calibrated to your industry.

The methodology that scores how recommendable a brand is to AI. The dimensions and weights are published. Which ones matter most in your category is the proprietary calibration, and it stays backstage.

24%

Foundation 24%

Structured identity signals across owned and earned media. Can the machines tell exactly who you are?

20%

Endorsement 20%

Third-party authority citations: reviews, references, credentialed mentions in the sources that count.

16%

Alignment 16%

Consistency between what the brand claims and what sources confirm. One story, everywhere.

16%

Presence 16%

Coverage density across the sources AI models trust and retrieve from.

12%

Warmth 12%

Sentiment quality of how the brand is characterized: conviction versus caveat.

12%

Constancy 12%

Recency and freshness of signals reaching model training and retrieval, every time.

The maturity ladder

Every brand is on a rung. The work is climbing it.

0–20

Unseen

AI cannot reliably name the brand.

21–40

Surfacing

Sporadic, inconsistent mentions.

41–60

Standing

Consistent citation in category context.

61–80

Trusted

Named reliably across engines.

81–100

The Answer

The machine names you first.

The intent framework

The ten questions every buyer asks AI.

We do not test a bag of random prompts. We score your brand across a structured, journey-mapped taxonomy, so you know exactly which buyer questions name you and which still belong to the category.

Stage
Intent category
The signal it tests
Awareness
Discovery & Category
Are you named in the unbranded category answer at all?
Awareness
Experience & Lifestyle
Do you own the experiential signals, not just the functional ones?
Consideration
Cost & Value
Is there a defensible value narrative the engine can cite?
Consideration
Fit & Qualification
Can AI match your specifics to a buyer's need?
Evaluation
Decision Criteria
Do you shape the criteria the buyer evaluates on?
Evaluation
Program & Differentiation
Is your signature strength legible to machines?
Evaluation
Trust, Safety & Reputation
Does third-party proof vouch for you?
Decision
Competitive & Comparison
Do you appear in the consideration set, ranked high?
Decision
Urgency & Trigger
Are you the answer in the time-pressured moment?
Authority
Founder & Authority
Is your origin and credibility fully indexed?

Geographic intent layers onto any category. Ten categories, five engines, one 100-point intent profile per brand.

The cadence

Re-scored and re-worked every quarter, never audited once.

Weeks 1–2

Re-score

Full audit across five engines. Compare to last quarter. Find the mover.

Week 3

Plan

Set targets. Sequence the work by weakest dimension. Sign-off.

Weeks 4–10

Build

We execute: earned media, schema, content, source seeding.

Weeks 11–13

Report

Executive scorecard. Lift captured. Next quarter set.

Proof

Two industries. Two problems. One instrument.

Case 01 · Women's health

The signal that compounds.

A national women's health brand was barely in the answer when buyers asked AI about its category. We engineered a five-part national data series through national wire, giving journalists a reason to cite and the engines a primary source to retrieve.

+600%
cumulative AI citation signal in 32 days
49 / 60
citation score across five engines · 82% of maximum
347
placements from one release · 85.4M monthly reach, anchored by Yahoo Finance and AP News
3 of 5
engines at 10+ of 12 · one holding a perfect score

Between waves, two engines softened while two recovered. A blended average would have hidden it. We score per engine, every time, so the dips were caught, diagnosed to specific buyer questions, and targeted in the next wave. Measurement is not a report card. It is the steering wheel.

Case 02 · Consumer health devices

The machines were citing the science. And crediting the wrong brand.

A global consumer health device brand had peer-reviewed clinical proof, and the engines were quoting it. But one engine credited a rival device with the brand's own results, every single time. We repaired the citation chain: a scientific release through national wire put the study on record as a brand-attributed primary source, and MedicalStudy and FAQPage schema connected the science to its owner.

20 / 60
baseline citation score, from zero, within 24 hours of first wire
1 → 7
fastest engine lift (of 12) after structured data shipped
90.5M
unique monthly syndication reach for the science release · 323 postings
Per engine
one engine still credits the rival · which is why we re-score, never audit once

Owned content alone could not fix this. The engines needed a trusted third-party record connecting the science to the brand. Wire-distributed primary source, schema markup, per-engine tracking to confirm the repair holds. That is the citation chain.

One brand needed volume: presence in the sources engines trust. The other needed precision: the right name attached to signals already flowing. The Maturity Method™ diagnosed both, sequenced the work by weakest dimension, and reported per engine, never blended. That is the difference between monitoring your AI visibility and moving it.

Why SignlLabs

The instrument, plus the team that moves the number.

01

Named methodology

A score and a ladder you can benchmark and cite, not a generic metric.

02

Prescriptive

We tell you the move, sequenced by your weakest dimension, then make it.

03

Industry-calibrated

Scored against the right benchmark for your category, not an average.

04

Full-service

We earn the media, build the schema, seed the sources. No SaaS tool can.

Let's make sure the machines name you first.

Get in touch and we'll walk you through a baseline audit across all five engines.

Contact us
Press & media kit

Everything a journalist or partner needs.

About SignlLabs (short)

SignlLabs is the AI Signaling practice that makes sure the machines name your brand first. Using the Maturity Method, SignlLabs measures how recommendable a brand is across ChatGPT, Claude, Perplexity, Gemini, and Copilot, then engineers the lift.

About SignlLabs (long)

SignlLabs is an agency-led AI Signaling practice powered by the Maturity Method, a zero-to-one-hundred methodology that scores how recommendable a brand is to AI across six dimensions and ten intent categories. Unlike monitoring dashboards, SignlLabs pairs the measurement with full-service execution: earned media, schema, content, and source seeding, re-scored every quarter.

Fast facts

5
engines scored
6
dimensions
10
intent categories
0–100
Maturity Method Score

Contact

erika.austria@gcw.agency
Answers

AI signaling, answered.

Short, source-ready answers to the questions buyers and journalists actually ask AI about our category.

What is AI signaling?

AI signaling is the practice of engineering how AI understands, trusts, and recommends your brand. The engines do not rank pages, they synthesize an answer and name a few brands. AI signaling is the work of becoming one of the names.

How is AI signaling different from SEO?

SEO optimizes for a page of blue links. AI signaling optimizes for the answer itself. The overlap is under twenty percent, because engines weight source credibility and third-party consensus, not keywords and backlinks.

Can a brand control what AI says about it?

Not directly. You cannot inform the model from your own website alone. Brand-owned content is only five to ten percent of what AI cites. The dominant lever is earned and paid media in the sources engines already trust.

What is the Maturity Method?

The Maturity Method is a zero to one hundred methodology that scores how recommendable a brand is to AI across six dimensions and ten intent categories, measured on all five major engines.

Which AI engines does SignlLabs measure?

SignlLabs scores your brand across ChatGPT, Claude, Perplexity, Gemini, and Copilot, in both base and search modes, and reports each engine separately rather than blending them.

How do you get recommended by ChatGPT and other AI?

Fix the owned foundation with schema and clear answer blocks, then earn citations in the third-party sources AI retrieves from: review platforms, communities, and trusted press. SignlLabs measures the gap and engineers the lift every quarter.