AI DISCOVERY · ACTION

>>> Chosen by AI agents.

Discovered, evaluated, and transacted, not just recommended.

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Authority
Shortlist

BRAND · SENTIMENT

>>> AI brand beliefs

72%

Favorable

10%

Unfavorable

81.6% of evaluations use prior beliefs

SHORTLIST · PICKS

>>> Agent selection rate

Shortlist picks over time

READINESS · SCORE

>>> Belief audit

76/100

Agent readiness

Retrieval
65%
Evaluation
80%
Retrieval, evaluation, and action readiness

What is Agentic Search Optimization?

ASO prepares a business so AI agents can evaluate and transact with it, not only recommend it. GEO ends with a human decision. ASO ends with the agent selecting a provider and completing the purchase or booking. Example: send gift cards to employees; the agent picks the vendor and buys.
RetrievalEvaluationActionSelection rate

AI reads more than humans

Agents do not only pick the top search result. Selection often comes from deeper in the list.

  • 38.2% of agent picks came from rank 4 or lower
  • Comparison articles and original research matter
  • Third-party mentions shape shortlists
  • Brand authority content builds retrieval signals

AI already has beliefs about brands

Most evaluations lean on prior brand beliefs, not only current SERP position.

  • 81.6% of evaluations used prior brand beliefs
  • Audit what AI believes about you vs rivals
  • Correct weak or inaccurate perceptions
  • Third-party validation strengthens trust

Agents act when transactions are easy

If an agent cannot complete the action, it often switches to a competitor.

  • 78.3% completion when pages are machine-actionable
  • 9.6% when they are not
  • Structured feeds, APIs, and readable forms
  • Success measured by selection rate, not rank alone

The three stages of agentic ASO

Be discoverable, define fit clearly, and make transactions callable by agents. We run that loop continuously: belief audits, suitability content, and machine-actionable paths.
  1. 1

    Retrieval

    Agents search the web and AI platforms, build shortlists, and lean on existing brand beliefs. We strengthen comparison content, PR, research, and authority signals so you enter the candidate set.

  2. 2

    Evaluation

    Agents score fit against hard requirements, nice-to-haves, and optional criteria. We build suitability pages covering who you are for, who you are not for, proof, and honest not-a-fit sections that increase trust.

  3. 3

    Action

    Agents must complete the transaction. We implement structured product feeds, APIs, machine-readable forms, and checkout flows so agents convert instead of bouncing to a rival.

How we build agentic ASO

Researchers ran 2,417 agentic tasks across ChatGPT, Gemini, Claude, Perplexity, and others. The pattern is clear: agents need to find you, believe you fit, and complete the action. We structure engagements around retrieval, evaluation, and action, not vanity rankings.
Overview

Discoverable, credible, and machine-actionable

Agentic Search Optimization is not classic app-store keyword spreadsheets. Agents need to find you (Retrieval), believe you fit (Evaluation), and complete the action (Action). GEO recommends to humans; ASO gets chosen and transacted by AI.

Researchers ran 2,417 agentic tasks across ChatGPT, Gemini, Claude, Perplexity, and others. The pattern is clear: suitability content will matter as much as landing pages do for SEO over the next two years.

We structure engagements around belief audits, suitability pages, and machine-actionable wiring, measured by selection rate and agent completions, not vanity rankings alone.

What we deliver

  • The core formula: be discoverable (Retrieval), define who you are for (Evaluation), and make transactions easy for AI (Action).
  • Over the next two years, suitability content will matter as much as landing pages do for SEO.
  • Third-party validation and PR grow in value as agents lean on prior brand beliefs.
Overview

Discoverable, credible, and machine-actionable

The core formula: be discoverable (Retrieval), define who you are for (Evaluation), and make transactions easy for AI (Action).

Focus: Selection rateStages: R → E → ACadence: Continuous
Retrieval

Enter the shortlist agents build

Agents search the web and AI platforms, then assemble a shortlist from what they find and already believe about your category. Comparison articles, original research, and authority content improve retrieval: 38.2% of picks in the study came from results ranked fourth or lower.

We audit what models believe about your brand versus competitors today: gaps, outdated positioning, and weak perceptions. Correction plans combine evidence, third-party validation, and PR citations agents can trust.

Retrieval work is continuous. After major positioning shifts, beliefs need refreshing so you stay in the shortlist before evaluation even starts.

RetrievalStage 1

Enter the shortlist agents build

Agents search the web and AI platforms, then assemble possible vendors from what they find and already believe.

Signals: Content + PRAudit: AI beliefsDepth: Rank 4+

What we deliver

  • Agents search the web and AI platforms, then assemble possible vendors from what they find and already believe.
  • Comparison articles, original research, and authority content improve retrieval. 38.2% of picks came from results ranked fourth or lower.
  • We audit what models believe about your brand vs competitors and correct weak perceptions with evidence and third-party validation.
Evaluation

Suitability pages that agents trust

Once you are on the shortlist, agents sort requirements into must-haves, important, nice-to-have, and optional, then pick the best overall fit. Clear use cases, industries, customer types, and problems solved led to 2.7× more selections in the study.

Suitability pages are structured for agent scoring: who it is for, who it is not for, hard versus nice-to-have capabilities, and proof agents can verify. Honest disqualifiers increase trust; vague marketing copy loses to competitors who state fit plainly.

We map features to buyer requirements, publish comparisons and case studies, and measure selection rate after suitability updates so you know what moved the needle.

What we deliver

  • Agents sort requirements into must-haves, important, nice-to-have, and optional, then pick the best overall fit.
  • Clear use cases, industries, customer types, and problems solved led to 2.7× more selections in the study.
  • Honest “not a fit when…” sections increased trust. Suitability beats vague marketing copy every time.
EvaluationStage 2

Suitability pages that agents trust

Agents sort requirements into must-haves, important, nice-to-have, and optional, then pick the best overall fit.

Fit clarity: 2.7× picksSections: For / not forProof: Comparisons
Action

Let agents finish the transaction

Selection without completion is a lost sale. Machine-actionable pages completed conversions 78.3% of the time in the study; non-actionable pages dropped to 9.6%, and agents often switched to a rival who made the transaction easy.

Structured product feeds, documented APIs, machine-readable forms, and frictionless checkout are required, not optional extras you add after launch. We wire the paths agents need so purchase, booking, or signup happens without a human in the loop.

Action readiness is tested end to end: feeds published, endpoints documented, checkout completable programmatically, and completion rate monitored against shortlist inclusion.

ActionStage 3

Let agents finish the transaction

Machine-actionable pages completed conversions 78.3% of the time. Non-actionable pages dropped to 9.6%, and agents often switched vendors.

Actionable: 78.3%Blocked: 9.6%Stack: API + feeds

What we deliver

  • Machine-actionable pages completed conversions 78.3% of the time. Non-actionable pages dropped to 9.6%, and agents often switched vendors.
  • Structured product feeds, APIs, machine-readable forms, and frictionless checkout are required, not optional extras.
  • We wire the paths agents need so selection turns into purchase, booking, or signup without a human in the loop.
Findings

What 2,417 agentic tasks revealed

The study across major AI platforms shows agents do not only choose the top search result. Deep retrieval and prior brand beliefs shape outcomes as much as position: 81.6% of evaluations start from what models already think about vendors.

Reputation and third-party validation matter more because agents lean on prior opinions before they ever hit your site. Future success is measured by how often AI chooses and acts on you, not traditional rankings alone.

Our reporting covers shortlist inclusion, selection rate, belief-audit deltas versus competitors, suitability coverage gaps, and action completion after APIs and feeds are wired, so you have a roadmap, not a one-off audit deck.

What we deliver

  • Agents do not only choose the top result. Deep retrieval and brand beliefs shape outcomes as much as position.
  • Reputation and third-party validation matter more because most evaluations start from prior model opinions.
  • Future success is measured by how often AI chooses and acts on you, not traditional rankings alone.
FindingsStudy

What 2,417 agentic tasks revealed

Agents do not only choose the top result. Deep retrieval and brand beliefs shape outcomes as much as position.

Tasks: 2,417Beliefs: 81.6%Deep rank: 38.2%
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