Phase II · Partnership Product · formerly AI Spin Tracker
AI Recall Tracker
Know when AI starts remembering your story — and whether your message pulls through.
Stuff That Spins turns your announcement into a structured Narrative Fingerprint. GEOGrow tracks when AI engines begin recalling it, what sources they cite, whether your brand gets credit, whether the story is remembered accurately, and whether the intended message actually pulls through into AI answers.
See when AI starts remembering your story — and whether it remembers the right version.
Traditional PR tracks coverage.
AI Recall Tracker tracks machine understanding.
Traditional PR asks: Did we get mentioned?
AI Recall Tracker asks: Did the message survive into LLM memory?
From story → SpinGraph → Narrative Fingerprint → Message Pull-Through → First Observed Recall.
This is not a clipping report.
We are not just asking where your press release appeared. We are asking:
- Do AI engines know the topic exists?
- Do they explain it correctly?
- Do they associate it with your brand?
- Do they repeat the message you intended?
- Do they preserve the proof points?
- Do they cite the right source?
- Do they keep the caveats intact?
- Do competitors intrude when AI explains your topic?
- When was accurate recall first observed?
- Is the answer improving, degrading, or drifting over time?
Message pull-through for the AI era
In traditional PR, message pull-through measures whether earned media repeated the story you wanted the market to understand.
AI Recall Tracker brings that same discipline to LLMs.
A model can mention your brand and still miss the point. It can know your announcement happened but drop the caveat, weaken the category association, cite the wrong source, or describe the story in a way that benefits a competitor.
That is why AI Recall Tracker measures more than visibility. It measures whether your message pulled through into machine-generated answers.
How we measure — and why you can check our work
Every tracked story gets a frozen prompt battery generated from its Narrative Fingerprint and Message Pull-Through Target.
We sample each engine multiple times per prompt and report distributions — for example, "recalled in 3 of 5 samples" — never a single cherry-picked answer.
Every score links back to raw, timestamped engine transcripts.
AI answers vary by phrasing, account, model, and day. Any vendor showing you one screenshot as proof is selling you noise.
AI Recall Tracker shows the pattern.
Stuff That Spins × GEOGrow
Grow Where AI Looks
Stuff That Spins structures the story. GEOGrow tracks AI recall.
Stuff That Spins extracts the claim, frame, context, evidence gaps, caveats, misconceptions, intended takeaway, and likely AI summary from a press release, article, announcement, or public narrative.
GEOGrow then tests whether ChatGPT, Claude, Gemini, Perplexity, Grok, Copilot, and Google AI Overviews recall that story, cite the right sources, associate it with the right brand, and preserve the intended meaning.
Together, they answer the question PR teams now need to prove:
Did the story enter AI memory — and did the right message come with it?
Traditional PR vs. AI Recall Tracking
Traditional PR tells you where the story appeared. AI Recall Tracker tells you whether the story entered machine memory — and whether AI remembers it correctly.
The Narrative Fingerprint
Before we track AI recall, we define exactly what AI should understand. For each tracked story, Stuff That Spins extracts:
Powered by SpinGraph narrative intelligence — the structured asset AI Recall Tracker monitors over time.
The Message Pull-Through Target
A story cannot be measured well unless the intended message is defined first. For every tracked story, AI Recall Tracker creates a Message Pull-Through Target:
Primary message
The main idea AI should remember.
Supporting message 1
The first proof point, context point, or category association AI should preserve.
Supporting message 2
The second proof point, audience segment, use case, or differentiator AI should preserve.
Required caveat
The limitation, legal nuance, condition, or warning AI must not drop.
Brand attribution requirement
How clearly AI should connect the story to the brand, product, research, executive, or announcement.
Claims to avoid
What AI should not exaggerate, imply, or invent.
Competitors to watch
Which competitors should not get credit for your story, claim, or category.
Ideal AI answer
A plain-language version of what a strong AI answer should say.
This becomes the benchmark for daily LLM tracking.
What AI Recall Tracker Measures
For every tracked story, AI Recall Tracker generates monitoring prompts and scores recall, comprehension, citation quality, and message pull-through across leading AI answer engines.
First Observed Recall
The first time an AI engine mentions the story, idea, claim, or topic.
First Accurate Recall
The first time an AI engine explains the story correctly.
First Brand Association
The first time AI connects the topic to your brand, product, research, executive, or announcement.
First Cited Recall
The first time AI cites a supporting source — SpinGraph page, press release, media coverage, owned content, or another authoritative source.
Message Pull-Through
Whether the intended message survives in AI answers — main point, proof, category association, brand connection, and caveat.
Core Message Retention
Whether the primary message appears accurately in the AI answer.
Supporting Message Retention
Whether the model includes the proof points, audience, use case, or differentiators that make the story matter.
Brand Attribution
Whether the model gives your brand credit for the story, claim, product, research, or category.
Category Association
Whether AI connects your brand to the category you are trying to own.
Claim Retention
Whether the core claim survives in AI answers without being weakened, exaggerated, or distorted.
Frame Retention
Whether AI preserves the intended framing or changes the story into something else.
Caveat Retention
Whether important limits, warnings, legal nuance, or conditions are preserved.
Citation Quality
Whether AI cites authoritative, brand-owned, media, third-party, weak, missing, or incorrect sources.
Competitor Intrusion
Whether competitors appear when AI explains your topic, category, claim, or announcement.
Memory Drift
Whether AI answers become more accurate, vague, distorted, overstated, or misattributed over time.
LLM Message Pull-Through Score
AI Recall Tracker scores every answer against the message you intended AI to remember.
Each story receives a Message Pull-Through Score:
Sample Recall Report
Per tracked story — powered by GEOGrow.
AI Recall Status
Your story is starting to appear in AI answers, but brand association is still weak and caveat retention is inconsistent.
30-day recall trend
AI Visibility
Pull-Through
AI understands the general category, but the intended message is only partially surviving. Models are recalling the product area, but they are weakening the brand association and dropping the regulatory caveat.
First Observed Recall
- Perplexity July 6
- Claude July 7
- ChatGPT July 9
- Gemini Not detected
- Google AI Overview Not detected
Recall Accuracy
Message Pull-Through
Target message
"Acme helps regional banks automate AI compliance evidence collection."
What AI currently says
"Acme provides compliance automation software for financial institutions."
Assessment: Partial pull-through. AI understands the category but weakens the regional-bank positioning and drops the evidence-collection language.
Missing from AI answers
Narrative Retention
Original frame "AI compliance automation for banks"
AI frame "Risk reduction and audit workflow automation"
Frame retention
Citation Map
What AI Gets Wrong
- Models understand the category but overstate the maturity of the product.
- Models mention the claim but drop the regulatory caveat.
- Perplexity cites media coverage but not the original source.
- ChatGPT describes the idea but does not consistently associate it with the brand.
- Grok frames the product as broader than the announcement supports.
- Category prompts sometimes surface competitors before the tracked brand.
Recommended GEOGrow Actions
- 1 Publish an owned explainer with direct definitions and entity clarity
- 2 Add FAQ schema addressing the regulatory caveat
- 3 Strengthen the SpinGraph page as a primary citation target
- 4 Create answer-ready content for high-intent prompts
- 5 Add a "What this does and does not mean" section
- 6 Build category-definition content that connects the brand to the intended market position
- 7 Re-test message pull-through after content updates
The mock above is illustrative. View the full sample report with engine matrix, recall timeline, raw transcripts, message pull-through scoring, and citation map.
Define → Structure → Test → Track → Improve
Define
Define the primary message, supporting messages, caveats, proof points, brand attribution requirements, and misconceptions to detect
Structure
Create a SpinGraph, Narrative Fingerprint, and Message Pull-Through Target
Test
Run AI recall prompts across major engines
Phase IITrack
Monitor recall, citations, accuracy, message pull-through, caveat retention, competitor intrusion, and drift
Phase IIImprove
Use GEOGrow recommendations to close the recall gap
Phase IIThen re-test to see whether AI understanding improved.
What Brands Track
Product launches
Did AI understand what the product does, who it is for, and why it matters?
Did the launch message pull through, or did AI reduce it to a generic product mention?
Category creation
Does AI associate your brand with the category you are trying to own?
Are competitors getting credit for the category instead?
Research papers
Do AI engines recall the finding accurately or overstate the result?
Do they preserve the methodology, caveats, and source attribution?
Crisis response
Has AI corrected the misinformation, or is it still repeating the wrong claim?
Is the corrected message pulling through into AI answers?
Regulatory announcements
Does AI preserve the legal or compliance nuance?
Does it drop important conditions, limitations, or disclaimers?
Thought leadership
Is your point of view appearing in answers about the topic?
Does AI attribute the idea to your brand or flatten it into generic industry commentary?
Competitive positioning
Are competitors being named instead of you when AI explains the idea?
Is AI associating your message, claim, or category with someone else?
Funding announcements
Does AI only remember that you raised money, or does it remember what the funding was meant to prove?
Analyst or benchmark reports
Does AI preserve the key finding, or does it turn the report into a vague market trend?
Brand Dashboard
Every brand gets a secure dashboard to manage tracked stories and monitor AI recall over time.
Brand-level view
Story table
Each tracked story includes:
Alert feed
AI Recall Tracker notifies you when something meaningful changes:
- • Claude recalled your story for the first time.
- • Perplexity cited media coverage but not your original release.
- • ChatGPT mentioned the category but did not associate it with your brand.
- • Grok overstated the claim and dropped the compliance caveat.
- • Gemini associated the topic with a competitor.
- • Google AI Overview surfaced the story but cited a weak source.
Individual Story Dashboard
Each story gets its own report.
Story overview
Recall timeline
See how AI memory forms over time:
Engine matrix
Compare performance across engines:
Raw transcripts
Every score links back to the raw AI answer, prompt, engine, date, and timestamp.
No black box. No cherry-picked screenshots. No hidden methodology.
Pricing Preview
Launch Package
$299/story · 30 days
SpinGraph + AI Recall Tracker — structure the story, define the Message Pull-Through Target, then prove when LLMs begin recalling it. Powered by GEOGrow.
Includes:
- • SpinGraph Report
- • Narrative Fingerprint
- • Message Pull-Through Target
- • Prompt battery
- • Daily AI recall checks
- • Engine matrix
- • Citation map
- • Raw transcripts
- • Email alerts
- • GEOGrow recommendations
SpinGraph Report
$99/report
Turn one announcement into a structured, citeable Narrative Fingerprint.
Includes:
- • Core claim
- • Supporting claims
- • Narrative frame
- • Spin tactic
- • Key entities
- • Missing context
- • Important caveats
- • Likely AI summary
- • Misconceptions to detect
- • Prompt battery preview
AI Recall Tracker
$299/story · 30 days
Recall tracking only — for stories already structured elsewhere. Powered by GEOGrow.
Includes:
- • Daily AI checks
- • Message pull-through scoring
- • Citation tracking
- • Engine matrix
- • Drift alerts
- • Competitor intrusion alerts
- • Raw transcripts
Agency Recall Monitoring
$999/mo
Track multiple stories, topics, brands, claims, and competitors across AI engines.
Includes:
- • Multi-story dashboard
- • Client-level reporting
- • Weekly digest
- • Message pull-through scoring
- • Citation monitoring
- • Competitor intrusion alerts
- • Exportable reports
Enterprise AI Memory Intelligence
$2,500+/mo
Ongoing AI recall, citation, drift, competitor intrusion, message pull-through, and narrative correction monitoring.
Includes:
- • Custom prompt batteries
- • Multiple brands
- • Multiple markets
- • Competitor tracking
- • Executive reporting
- • GEOGrow strategy recommendations
- • Ongoing AI memory improvement workflow
When AI remembers the wrong version
Your story entered AI answers — but recall is partial, citations are weak, attribution is missing, or the framing drifted. AI Recall Tracker surfaces the gap between what you intended and what the machines remember.
GEOGrow recommends fixes:
- Stronger entity clarityOwned explainersFAQ-style answersImproved schemaSupporting contentCategory-definition pagesCitation-path improvementsPrompt-targeted contentSpinGraph strengtheningCompetitive positioning content
Then we re-test whether AI understanding improved. Because the goal is not just to be mentioned by AI. The goal is to make sure the right message pulls through.
See sample SpinGraph + GEOGrow layer → Join the Phase II waitlist →Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO