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Product Vision · In development

AI now decides who gets to sell.

Today by recommending, tomorrow by buying.

Strajist measures how AI sees your brand today. We are building the next layer: a way to run the buying conversation your customers have with AI, many times, as the buyer types you sell to, and report which product gets chosen, how often, and why.

In development. Design partners onboarding now.

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Why visibility isn't enough

You can be everywhere AI looks — and still lose every deal.

A brand can rank in every AI model it is checked in, be mentioned hundreds of times a week, and still never get recommended at the moment a buyer or an agent decides. Visibility is necessary. It is not what wins.

01
Visibility — present
"You're mentioned in every model you track this week."
02
Perception — gap
"Some of them still call you 'expensive and slow'."
03
Decision — loss
"Across 100 buying conversations, AI chose your competitor 60 times. You, 34."
What we're building

We turn the AI answer into a number you can work with.

A model does not answer the same way twice. One answer is an anecdote. So the same buying scenario runs many times, using the personas of the buyers a company actually sells to, and the output is a rate you can track over weeks, not a single answer you have to trust.

  1. 01 · Simulate

    Run the buying conversation, many times

    • The same buying scenario, run as each buyer type you sell to
    • Mentioned, in what position, actually recommended, and who was chosen instead
    • The exact sentences the models produce, stored with the question that produced them
  2. 02 · Explain

    Find out why you lost

    • What each model believes about category, pricing, audience and positioning
    • Which web pages shaped that belief, and which of them are wrong
    • Where the company describes itself differently from how it is described
  3. 03 · Fix and re-run

    Change it, then prove it changed

    • A prioritised task list: which page, which sentence, which source
    • The next run is measured against the last one
    • A movement is reported only when it is larger than the normal noise of that metric
What a report will show · Sample

Chosen in 34 conversations out of 100.

Each run says which product was chosen, how often, and what drove the outcome, so the task list starts from the reason and not from a guess.

And why

  • Price framing reads as premium with no reason attached
  • Two review pages still describe last year's feature set
  • The competitor is carried by three sources the models trust
  • Your product 34
  • Competitor A 60
  • Everyone else 6
Illustrative figures. A real run reports your buying scenarios, your personas and your competitor set.
Roadmap

From being seen to being chosen.

We started with visibility because that is where the market started. Every step since has moved closer to the moment a buyer decides.

  1. Live

    AI Visibility

    Are we there at all?

    Which models name the product, in what position, next to which competitors.

  2. Live

    AI Perception

    What is said about us, and is it true?

    Category, pricing, audience and positioning as the models describe them, against what the company actually says.

  3. In development

    Agent Decision

    Are we chosen, and at what rate?

    The same buying scenario run many times against buyer personas, reported as a selection rate with the drivers behind it.

  4. Next

    Decision Engineering

    What should we change to be chosen more often?

    Price, packaging and positioning varied inside the simulation, so the answer arrives before the change ships.

  5. Later

    Representation

    Who argues our case when no human is in the room?

    A seller side agent that answers a buyer agent accurately, inside limits the company sets.

Visibility is where it starts. The decision is where it pays.

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