Tools | Mike Schneider

AI visibility snapshot.

Buyers ask ChatGPT and Perplexity about you before they ever reach your site. The snapshot shows you what those systems actually say: how they describe you, whom they compare you to, and what they get wrong.

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The 4-Fits self-assessment.

Product, market, channel, model. Score yourself in two minutes and see which fit is holding growth back. Bring the result when we talk.

Free self-assessment

Where are you stuck?

Great product, stuck growth? Usually the problem is not the product. Sustainable growth needs four fits working as a cycle: Product to Market, Product to Channel, Channel to Model, Model to Market. A break in any one stalls the whole loop. Score yourself in two minutes and see which fit is holding you back.

How to use this A diagnostic, not a pass/fail. Honest answers beat optimistic ones.

Each fit has 7 yes/no questions. Check the ones that are true today. Score = % checked per fit.
A low score on a fit does not mean you are failing. It means that is where the leverage is. Your highest-leverage move is your lowest-scoring fit.
Answer for your business as it is today, not as you hope it will be. The diagnostic only works when the answers are honest.
Load the sample scenario to see a worked example, then clear it and score your own.

What the 4 Fits framework says

Most founders focus only on Product/Market fit. But sustainable growth needs 4 fits in a cycle, and a mismatch between any two breaks the whole loop. Most "stuck despite a good product" symptoms come from one of the other three fits, not from PMF.

AI-era adaptation: each fit gets a new dimension because of how buyers research and buy when AI agents mediate the journey. The cycle still holds. The surfaces just shifted.

Product ↔ MarketOutcome value the market wants, agent-discoverable

Product ↔ ChannelChannels reach buyers where they actually research

Channel ↔ ModelPricing + sales model match channel economics

Model ↔ MarketMarket structure + budget support unit economics

Scenario name:

Quick-load a sample

Load a worked example to see how scoring plays out. Edit any item after loading, or clear it and score your own.

The cycle, visualized

The 4 fits operate as a cycle: each fit feeds the next. A weak fit anywhere breaks the loop. Boxes fill from the bottom as you check items; arrows show cycle direction; a dashed border flags a weak fit (score < 3/7).

Check items in the assessment below to see the cycle fill in. Your lowest-scoring fit is where to focus.

Assess the 4 fits

Check each item that is true today. Be honest. The diagnostic is more useful when the answers reflect reality, not aspiration.

Fit 1 · Product ↔ Market

Product ↔ Market

Does the product solve a problem the market actually has, in a way the market values? The classic PMF lens.

AI-era twist: "Market" now includes AI agents acting on behalf of buyers. The product must be discoverable and assessable by agents during the research phase, not just by humans during a sales conversation. If your category is researched via ChatGPT, Claude, or Perplexity, you have to be in those results, and agent-readable on your own surfaces.

Product delivers measurable outcomes within ~90 days for typical buyers
Buyers describe the problem in language similar to how you describe the solution
Reference customers exist who would evangelize unprompted
AI agents researching the category surface your brand (LLM-visibility audit passes)
Product's value compounds over time (not single-use / point-in-time)
You can attribute outcomes back to product use (not just correlation)
Buyers stay (low churn) even when alternatives are cheaper

Score

0/7

Score 0/7 · check items above to assess

Fit 2 · Product ↔ Channel

Product ↔ Channel

Does the product fit the channels you reach the market through? Built for the channel, not retrofitted.

AI-era twist: Channels now include LLM-citation surfaces (ChatGPT, Claude, Perplexity, Gemini), agent-callable MCP endpoints, and structured-data feeds. If buyers' agents research the category, your product must show up there, which requires structured context (JSON-LD, /llms.txt, schema.org, MCP servers) plus content that compounds in those surfaces.

Channels reach where buyers actually research (not just where you've historically distributed)
Content compounds: each new piece improves discoverability of earlier pieces
LLM citations (ChatGPT/Claude/Perplexity) reference your brand for relevant queries
Structured data (JSON-LD, /llms.txt, schema.org) is deployed + indexable
MCP endpoints or agent-callable surfaces exist (if relevant for category)
Channels match buyer behavior: if buyers ask agents, your agent surface is present
Content velocity matches channel decay (channels stay alive)

Score

0/7

Score 0/7 · check items above to assess

Fit 3 · Channel ↔ Model

Channel ↔ Model

Does your pricing and sales model match the channels' economics? CAC, cycle, and ACV all need to align.

AI-era twist: Outcome-priced and token-priced models match forward-deployed channel economics. Seat-based pricing breaks when buyers expect value-priced. Product-led channels can't sustain enterprise sales cycles. Your pricing model needs to survive the outcome-pricing shift if your category is moving that way.

CAC math works given channel conversion rates
Sales cycle length is compatible with channel velocity
Pricing model matches buyer expectations from the channel (product-led ≠ enterprise sales-led)
Free / paid mix is consistent with channel norms
Channels produce buyers with the right ACV for the pricing tier
The sales process is the right shape for the channel's volume + cycle
Pricing model survives the outcome-pricing transition (if the category is moving that way)

Score

0/7

Score 0/7 · check items above to assess

Fit 4 · Model ↔ Market

Model ↔ Market

Does the business model match the market's structure, willingness to pay, and expansion path?

AI-era twist: The market must have measurable outcomes to support outcome-pricing. Markets framed as "headcount replacement" are commoditizing fast (a race to token-pricing zero); markets framed as "AI-augmented capability" have durable budgets. Your pricing model needs an expansion path that survives the AI commoditization wave.

Market has measurable outcomes you can price against
Market budget is growing (or your share of it is)
Market structure supports your unit economics (LTV/CAC ≥ 3)
Buyers value-perceive the pricing model (no friction at contract stage)
Market expects AI-augmentation (your offering frames well) vs. headcount-replacement (frames poorly)
Pricing model has an expansion path (seats → outcome → multi-product)
Model is durable against token-economics commoditization

Score

0/7

Score 0/7 · check items above to assess

Overall fit profile

The shape of the 4 fits side by side. The largest gap is your highest-leverage focus area.

Fit profile

Product ↔ Market

Product ↔ Channel

Channel ↔ Model

Model ↔ Market

Focus area: Check items above to surface where the highest leverage is. Your lowest score is where to focus.

Your weakest fit is flagged above. Bring it when we talk and I will walk through it with you.

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Math model: each fit scores 0–7 based on the number of items checked. The % shown is (score / 7) × 100. "Focus area" surfaces the lowest-scoring fit. "Strong" when a fit is ≥ 6/7; "mid" when 3–5; "weak" when ≤ 2. Cross-fit dependencies aren't modeled; the fits interact as a cycle, but this tool scores each independently and lets you read the pattern across them.

Not modeled: competitor comparison; market-level fits separate from buyer-level fits; time dynamics (a fit can shift as the market matures); product roadmap dependencies (fixing a fit may require product change, not just GTM change).

FIG. 1Four-fits self-assessment. Runs locally; nothing is collected.

Working utilities.

AI visibility snapshot.

The 4-Fits self-assessment.

Where are you stuck?

What the 4 Fits framework says

The cycle, visualized

Assess the 4 fits

Product ↔ Market

Product ↔ Channel

Channel ↔ Model

Model ↔ Market

Overall fit profile

Fit profile