Narrative Market Fit: The Version of Product–Market Fit That Decides Whether AI Repeats You

Narrative Market Fit (NMF) is the point at which the market — and the AI models that now mediate it — repeat your company's framing of its category accurately and unprompted. It is the founder-era successor to product–market fit, and a framework from Signal Fidelity Group. Product–market fit asks whether people will buy what you built. Narrative Market Fit asks a newer, sharper question: when buyers, reporters, partners, and the AI models they consult describe your category, do they use your words — or someone else's?

Product–market fit was necessary. It is no longer sufficient.

For two decades, product–market fit was the only fit that mattered: build something people want, and growth follows. That hasn't changed. What has changed is where the buying journey begins. Before a prospect reaches your website, a salesperson, or a review, they ask an AI — 'What are the best tools for X?', 'Is this company any good?', 'Who competes with the incumbent?' The model answers in seconds, with confidence, and without you in the room. That answer is the new first impression, and it is assembled from whatever the model has already absorbed about your category.

If the model absorbed your framing, you are the answer. If it didn't, you are a footnote to someone else's framing — or you are absent entirely. Product–market fit gets you customers who try you. Narrative Market Fit determines whether the market shows up already believing what you want it to believe.

What Narrative Market Fit is

Narrative Market Fit has three properties. It is accurate — the market repeats your claim without distortion, with no drift in scope, numbers, or category. It is unprompted — people and models reach for your framing on their own, not because you fed it to them in the moment. And it is durable — the same framing survives across surfaces, from a journalist's article to a competitor comparison to a model's answer, because it has been corroborated often enough to become the default.

This is not share of voice. Share of voice measures volume: how loud you are. Narrative Market Fit measures fidelity: whether the meaning that travels is the one you intended. In an AI-mediated market, fidelity beats volume, because a model does not weight every mention equally — it converges on the most consistent, most corroborated account of your category and repeats that one.

PMF makes people buy. Narrative Market Fit makes the market repeat you when you're not in the room.

The test: ask the models

Narrative Market Fit is measurable, and the test takes ten minutes. Open ChatGPT, Perplexity, Gemini, and Copilot and ask each the questions your buyers ask: 'What are the leading companies in my category?', 'What is this company, and who is it for?', 'Who should I use for the job I do?' Then score three things: Are you named? Is your framing — your category language, your differentiation — present in the answer? And are the facts correct?

We call the result your Share of Model: the portion of AI-generated answers about your category that reflect your framing and name you. Capture it today as a baseline, then re-run it weekly. Share of Model rising is Narrative Market Fit forming. Share of Model flat — while you publish and ship — means the market is hearing you but the models aren't keeping your account, which is almost always a corroboration or consistency problem, not a volume one.

The four inputs to Narrative Market Fit

Four inputs produce Narrative Market Fit, and each one can be engineered.

• Claim. A crisp, ownable statement of who you are and the category you lead — expressed as a fact a AI system can store: subject, predicate, object. Vague positioning produces vague triples; sharp claims produce repeatable ones.
• Corroboration. Third parties repeating your claim. A model trusts a claim that appears consistently across many independent sources far more than one that lives only on your homepage.
• Consistency. The same name, the same definition, the same category language, everywhere. Entity consistency is what lets a model bind every mention to a single account of you, instead of several blurred ones.
• Category language. The words the market uses for the problem. Supply them and the model adopts your vocabulary; cede them and you end up described in your competitor's terms.

Why the window is now

Narrative Market Fit compounds, and it rewards whoever defines the category first. As agents take on more of discovery — and, increasingly, action — the account a model holds of your category becomes the channel through which buyers find you. Early, canonical definitions get ingested and repeated until they harden into the default answer. Latecomers don't get a blank page; they have to dislodge an answer the model already gives confidently. The cheapest moment to own your narrative inside the models is before the category's answer sets.

Engineering Narrative Market Fit with FoundersTriple

FoundersTriple is built to manufacture Narrative Market Fit. It turns a founder's positioning into the semantic triples models store, plants those facts where the models look, and corroborates them across the sources models trust — so that when a buyer asks, the answer that comes back is yours. Positioning becomes model memory. The narrative becomes the distribution.

Further reading

From Signal Fidelity Group: 'Semantic Triples: Why AI Recommends Some Founders and Forgets the Rest' (the unit of fact behind your triples); 'Founders AEO & GEO: How to Show Up When Your Buyers Ask AI About You' (the practice); and 'Bio-Silicon Isomorphism: Why the Same Sentence Wins the Human and the AI system' (why one engineered sentence can win both a reader and a model).