The Citable M.A.P. Framework: How AI Actually Talks About Your Brand

How AI Systems Remember, Trust, and Recommend Your Brand Across Different People Over Time

AI engines like ChatGPT, Claude, Perplexity, Gemini, and Google's AI Overviews have evolved far beyond simple search results. They're now personalized answer machines that build memories of users and adapt their responses over time. Understanding how they work requires a fundamentally new approach to visibility.

Why AI Visibility Needs a New Playbook

The current narrative around Generative Engine Optimization (GEO) focuses primarily on:

"Optimize content so models cite you more."

This is important, but incomplete.

What actually matters for your brand is:

Who the AI model is talking to (persona, region, conversation history)
What it remembers about that person
Whether you are the answer that fits that person's story

This is AI visibility as Citable defines it:

AI visibility = how consistently AI systems remember, trust, and recommend your brand across different people, personas, and questions over time.

To make this operational, Citable introduces the M.A.P. Framework:

M.A.P. = Memory Fit, Authority Graph, Prompt Surface

It's your mental model for how AI engines "see" you—not in the abstract, but from the point of view of actual, long-lived users with unique personas.

The Three Pillars of M.A.P.

1. Memory Fit – How AI Treats You as It Gets to Know Someone

Definition: Memory Fit measures how a model's answers about your brand evolve for a specific persona as it builds up long-term memory of that persona.

In simpler terms: If an AI gets to know a "B2B CMO persona" over weeks of questions, does it talk about you in a better, more accurate way? Or do you vanish from the story?

What Citable Looks At:

Persona-specific story: Does a CMO persona get a different narrative than, say, a scrappy founder or a student researcher? Do those narratives match how you actually want to be positioned?

Memory trajectory: As the persona keeps asking related questions over days/weeks, do answers trend toward your Canonical Core or drift away into competitors and random alternatives?

Contradictions over time: Does the same persona get conflicting explanations of who you are, what you do, or where you fit?

Why It Matters:

LLM research is rapidly moving toward lifelong personalization—models that keep user-specific memories and use them heavily in future answers. If you only optimize for a cold, stateless prompt, you're missing the reality: AI will answer based on "who it thinks this person is" and what it already showed them.

Memory Fit is how you win in that world.

2. Authority Graph – Who Backs Up Your Story

Definition: Authority Graph is how strongly you are anchored in the high-signal sources and contexts that models lean on when deciding what (and whom) to trust.

It's not just "do we have backlinks?" but "who is vouching for us, in what context, and around which ideas?"

What Citable Looks At:

High-trust domains: Mentions in reputable news, .org/.edu sites, industry publications, documentation sites, standards bodies, and expert blogs.

Topical co-occurrence: Whether you're consistently mentioned next to the problems, categories, and use-cases you want to own.

Trust spine: The minimal set of high-authority sources that, together, give models a solid backbone of confidence about your brand.

Why It Matters:

GEO is shifting from "rankings" to reference rates—how often models choose you as a source in generated answers. A strong, coherent Authority Graph makes you the default choice when the model wants to cite or imply someone credible in your niche.

3. Prompt Surface – Where You Can Actually Be the Answer

Definition: Prompt Surface is the set of questions, intents, and scenarios where AI systems could plausibly recommend you—and whether they actually do.

If people are asking, "What should I use for X problem?" how often are you the one AI suggests?

What Citable Looks At:

Answer Footprint: For which queries and intents do you already appear? Where are you conspicuously absent?

Persona + geography: Does a US-based founder get a different recommendation than a European marketing lead? Are you visible in one region but ghosted in another?

Job-to-be-done coverage: Do models suggest you for the strategic "jobs" you care about—or pigeonhole you into outdated or narrow use-cases?

Why It Matters:

You can have strong Memory Fit and a solid Authority Graph, but if your Prompt Surface is tiny, you're effectively invisible. AI visibility is where memory + authority intersect with real questions.

Citable's Vocabulary for AI Visibility

To make all this usable, Citable defines a shared set of terms teams can think and talk with:

Core Concepts

Canonical Core: The 10–20 non-negotiable facts about your brand that every model should get right, across personas and engines.

Answer Footprint: The map of queries, engines, personas, and regions where AI currently mentions or recommends you.

Persona Mirage: When different personas receive conflicting or misaligned stories about who you are—e.g., "enterprise-grade" in one context and "just a side-project tool" in another.

Visibility Decay: The gradual erosion or distortion of your presence in answers over time when you're not actively refreshing signals.

Semantic Neighbourhood: The cluster of brands, concepts, and topics that models associate with you in embedding space—your "AI adjacency graph."

Contradiction Sink: When conflicting information about you exists online, models quietly down-rank or ignore you in favor of entities with cleaner, more consistent signals.

5 New Citable-Only Concepts

1. Memory Trajectory

How a model's answers about your brand change for a single persona as more interactions accumulate—are you moving toward sharper relevance or drifting into the background?

2. Persona Gravity

The "pull" your brand has on a given persona once they've encountered you a few times. High Persona Gravity means the model increasingly reaches for you as the default recommendation in your niche.

3. Alignment Window

The range of questions where a persona both knows you exist and sees you as a good fit. Outside this window, the model either forgets you or prefers someone else.

4. Trust Spine

The minimal set of high-authority, high-signal sources that, taken together, anchor your brand's identity inside AI systems. Lose the spine and your story collapses.

5. Prompt Delta

The difference in answers to the same prompt across personas, engines, or time slices. Large Prompt Deltas usually signal Persona Mirages, weak Canonical Core, or a broken Authority Graph.

Citable's Laws of AI Visibility

Citable wraps the framework in a few simple laws that teams can remember:

Law 1: Models answer like they remember, not like they index

Long-lived personas with memory will get different answers. If you only optimize for cold prompts, you're optimizing for the wrong world.

Law 2: Consistency beats volume

A small, coherent Trust Spine and clean Canonical Core will outperform noisy, conflicting coverage.

Law 3: Unclaimed jobs never surface

If you don't clearly claim a problem and show up in the right Semantic Neighbourhood, models will route that intent to someone else.

Law 4: AI visibility is persona-relative

Being "the answer" for one persona tells you almost nothing about how you show up for others.

Law 5: Visibility decays unless maintained

Models drift. The web changes. Competitors move. Without deliberate refreshes, your Memory Trajectory bends away from you.

How Teams Use M.A.P. with Citable

In practice, the Citable M.A.P. Framework gives teams a concrete way to run AI visibility like an actual discipline:

1. Measure

Spin up long-lived personas and track Memory Trajectory, Persona Gravity, and Prompt Delta across engines
Map your Authority Graph and Trust Spine around core topics
Chart your Answer Footprint and Alignment Window across jobs and regions

2. Diagnose

Spot Persona Mirages and large Prompt Deltas
Find gaps in your Canonical Core and contradictions feeding the Contradiction Sink
Identify where Visibility Decay is already happening

3. Improve + Monitor

Ship targeted content, structural fixes, and distribution moves that reinforce your Trust Spine and expand your Alignment Window
Re-run the same personas and prompts over time to see whether Memory Fit, Authority Graph, and Prompt Surface are actually improving

The Result: Precise, Data-Driven AI Visibility

Instead of "hoping AI mentions us," teams can now talk in precise terms:

"Our Memory Fit for founders is solid, but our Persona Gravity for CMOs is weak. Let's strengthen the Trust Spine around that use-case and widen the Alignment Window for GTM-focused queries."

That's the whole point of the Citable M.A.P. Framework: turning AI visibility from fuzzy anxiety into a system you can name, measure, and systematically bend in your favor.

Getting Started with M.A.P.

Ready to apply the M.A.P. Framework to your brand? Here's how to begin:

Audit your Canonical Core: List the 10-20 essential facts about your brand. Test them across AI engines.
Map your current Answer Footprint: Run key category queries and see where you show up (or don't).
Identify your Trust Spine: Which 5-10 high-authority sources consistently mention you? Are they aligned with your positioning?
Create test personas: Build 2-3 distinct personas and track how AI engines respond to them over time.
Measure, don't guess: Use Citable to track Memory Trajectory, Persona Gravity, and Prompt Delta systematically.

The AI visibility landscape is evolving rapidly. The brands that win will be those who understand not just what AI says about them, but how and why it says it—to different people, in different contexts, over time.

Welcome to the era of M.A.P.-driven GEO.

About Citable

Citable is the first AI visibility platform built specifically for the M.A.P. Framework. We help brands measure, diagnose, and improve how AI engines remember, trust, and recommend them across personas, regions, and time.

Ready to see your M.A.P. scores? Start your free trial today.