Polenist

GEO · Generative Engine Optimisation

AI visibility for Aerion

A GEO programme that makes Aerion appear accurately and consistently in ChatGPT, Claude, Gemini and Perplexity answers: technical foundation, source authority, content engine and a monthly measurement cycle.

Prepared by

Polenist

Prepared for

Aerion

v1.0 · 14 July 2026 · Valid for 30 days

Polenist makes brands visible

Polenist designs GEO processes that make brands appear in AI assistant answers with accurate information and a consistent narrative. Technical accessibility, source authority, content architecture and regular measurement are handled together.

  1. 01Accurate sources create visibility.
  2. 02A consistent narrative builds trust.
  3. 03Continuous measurement preserves visibility.

Preliminary analysis

Where Aerion stands today

AI visibility
Across the tested queries, Aerion appears in none of the four AI assistants.
Source presence
There is no Aerion record in the directories, media and reference sites that feed AI models.
Technical setup
The website carries no configuration specific to AI access.

A zero baseline is a clean start. The narrative can be built correctly from the beginning, with no misinformation to clear up first.

Four phases

Scope in numbers

AI platforms

phases

months of setup

ChatGPT, Claude, Gemini and Perplexity are the four surfaces the programme is built and measured against. Every work item behind these numbers is listed in the detail pages of this proposal.

Engagement model

Setup

Phases 1–3

One-off implementation · 4 months

Technical foundation, source and authority building, and the content engine, delivered as a single implementation programme.

Monthly process

Phase 4

Ongoing service · monthly cycle

Query testing, mention and accuracy tracking, gap analysis, optimisation actions and a monthly action plan.

GEO is not a one-off exercise. AI models update continuously and the sources behind their answers change, so the system built during setup is fed and measured on a monthly rhythm.

Timeline

  1. Month 1

    Phase 1 and the start of Phase 2

    Foundation live · first registrations

  2. Month 2

    Phases 2–3 continue, Phase 4 begins

    First content set · first report

  3. Month 3

    Phase 2 completes

    Source network in place

  4. Month 4+

    Phase 3 completes, monthly cycle continues

    Measure · produce · optimise

Setup runs across months 1–4. The monthly cycle starts in month 2 and continues from there.

/delivery

Visibility horizon

2–3 months

Real-time search surfaces

Platforms that read the live web while they answer — Perplexity and ChatGPT search — are where the first results are expected, once the technical foundation and the first source registrations are in place.

6–12 months

Model training data

Durable visibility inside model training data builds over a longer horizon, as the source network and the content base are picked up across successive model updates.

These horizons are an expectation, not a guarantee. AI platforms choose their own sources and update on their own schedule; what the programme controls is the accuracy, consistency and reach of the material they read.

Fees & terms

3,600 USDT setup · 800 USDT per month

Setup
Phases 1–3, planned as a four-month implementation: technical foundation, source and authority building, content engine. Billed at 1,200 USDT per month across the first three months.
Monthly
Phase 4, monthly visibility management: testing, tracking, gap analysis, optimisation actions and a monthly action plan. Invoiced from month 4 onwards, with no fixed end; the monthly work in the first three months is covered by the setup fee.
Terms
All fees exclude VAT. PR and media placement budgets and third-party platform fees are out of scope, reported separately for approval.

Next steps

  1. 01Review this proposal and the six detail pages, then approve it.
  2. 02Hold the kickoff meeting and plan access and permissions.
  3. 03Start the Phase 1 implementation.

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